cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2k0mfn
title: CCDC 2354091: Experimental Crystal Structure Determination

description: Related Article: Sebastian Steiner, Tobias Kuhn, Christoph Jessen, Valentin Bockmair, Alan Virmani, Andreas J. Kornath|2025|J.Am.Chem.Soc.|147|8361|doi:10.1021/jacs.4c15975
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: 10.15468/dl.du84w9
title: Occurrence Download

description: A dataset containing 465 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (China, India)",
 "TaxonKey is Carpodacus trifasciatus J.Verreaux, 1871"
 ]
}

 The dataset includes 465 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000420-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.thaxqs
title: Occurrence Download

description: A dataset containing 379 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Machine Observation)",
 "Geometry POLYGON((49.99 24.03,111.86 24.03,111.86 52.97,49.99 52.97,49.99 24.03))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Rhododendron arboreum Sm.",
 "Year 1970-2025"
 ]
}

 The dataset includes 379 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000421-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57745/5lezbf
title: READ_ME.txt

description: This file describes the experimental conditions and variables/columns used in the dataset.
DOI: 10.57745/6xgwyn
title: Vegetables crop calendar under grreenhouse_V3.csv

description: Dataset
DOI: 10.57745/giw36e
title: Vegetables crop calendar under grreenhousse_V2.csv

description: Dataset
DOI: 10.57745/yvsao5
title: Vegetables crop calendar under grreenhouse_V4.csv

description: Dataset
DOI: 10.57745/ldlqrv
title: READ_ME_2.txt

description: This file describes the experimental conditions and variables/columns used in the dataset.
DOI: 10.57745/cff07m
title: Vegetables crop calendar under greenhousse-2.csv

description: Dataset
DOI: 10.15468/dl.ftsp8t
title: Occurrence Download

description: A dataset listing the 1 species recorded in GBIF matching the query:
{
 "and" : [
 "Continent is Europe",
 "Country is France",
 "Geometry POLYGON((-5.10048 47.72143,-1.13949 47.72143,-1.13949 49.03427,-5.10048 49.03427,-5.10048 47.72143))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Reynoutria japonica Houtt.",
 "Year 2024-2025"
 ]
}

 The dataset's 1 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000413-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15063386
title: Study Results of Bachelor Thesis "Effects of vertical price location on the perception of supermarkets: A virtual reality study including eye tracking"
DOI: 10.5281/zenodo.15063387
title: Study Results of Bachelor Thesis "Effects of vertical price location on the perception of supermarkets: A virtual reality study including eye tracking"
DOI: 10.15468/dl.4rxstw
title: Occurrence Download

description: A dataset containing 1013 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Snow Entomological Museum Collection",
 "TaxonKey is Anthribidae"
 ]
}

 The dataset includes 1013 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000422-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2wm3jq
title: Occurrence Download

description: A dataset containing 38 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Carpodacus vinaceus formosanus Ogilvie-Grant, 1911"
 ]
}

 The dataset includes 38 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000423-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15045965
title: Gibbon dataset - PAMGuard deep learning tutorial

description: Deep learning and bats - PAMGuard tutorial dataset

General Information

Overview

This is a dataset to be used in part three of the PAMGuard tutorial *Deep learning in PAMGuard * (www.pamgaurd.org). The dataset is from a 4 channel TREX recorder deployed in Xe Sap national park in Laos. 

Year of data collection

2024

Geographic location of data collection

Xe Sap national park, Laos

Funding sources that supported the collection of the data

Arcus Foundation grant “Production of Acoustic Recorders for Gibbon Surveys”.

Recommended citation for this dataset

10.5281/zenodo.15045966

Data & File Overview

Description of dataset

These data contain a small raw data sample from an experimental 4 channel trex recorder along with a PAMGuard compatible deep learning model which can detect three types of Northern white-cheeked gibbon (_Nomascus leucogenys_) calls. The deep learning model was devleoped by Arbimon (www.arbimon.org). 

Files

The files are 4 channel .wav files made on a Trex recorder. These are 16 bit files with an unknown clip levels. 

The folder structure is as follows:

```md├── wav                                         #Folder of .wav files│   └── 20240205_230252.wav            #Sample wave file├── README.md                                   #This readme file├── gibbon_model.zip     #PAMGaurd deep learning model└── ```

Device locations

Confidential

Sharing and access

These data are open source under Creative Commons Attribution 4.0 International. This means that these data can be used in other tutorials as long as the original authors are credited. Using these data for scientific purposes requires the permission of the authors. 

```
DOI: 10.5281/zenodo.15045966
title: Gibbon dataset - PAMGuard deep learning tutorial

description: Deep learning and bats - PAMGuard tutorial dataset

General Information

Overview

This is a dataset to be used in part three of the PAMGuard tutorial *Deep learning in PAMGuard * (www.pamgaurd.org). The dataset is from a 4 channel TREX recorder deployed in Xe Sap national park in Laos. 

Year of data collection

2024

Geographic location of data collection

Xe Sap national park, Laos

Funding sources that supported the collection of the data

Arcus Foundation grant “Production of Acoustic Recorders for Gibbon Surveys”.

Recommended citation for this dataset

10.5281/zenodo.15045966

Data & File Overview

Description of dataset

These data contain a small raw data sample from an experimental 4 channel trex recorder along with a PAMGuard compatible deep learning model which can detect three types of Northern white-cheeked gibbon (_Nomascus leucogenys_) calls. The deep learning model was devleoped by Arbimon (www.arbimon.org). 

Files

The files are 4 channel .wav files made on a Trex recorder. These are 16 bit files with an unknown clip levels. 

The folder structure is as follows:

```md├── wav                                         #Folder of .wav files│   └── 20240205_230252.wav            #Sample wave file├── README.md                                   #This readme file├── gibbon_model.zip     #PAMGaurd deep learning model└── ```

Device locations

Confidential

Sharing and access

These data are open source under Creative Commons Attribution 4.0 International. This means that these data can be used in other tutorials as long as the original authors are credited. Using these data for scientific purposes requires the permission of the authors. 

```
DOI: 10.15468/dl.bkz3zy
title: Occurrence Download

description: A dataset containing 232090 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Denmark",
 "OccurrenceStatus is Present",
 "CoordinateUncertaintyInMeters is less than 50000",
 "Year is 2024",
 "DecimalLongitude is greater than or equal to 7.38",
 "DecimalLongitude is less than or equal to 9.03",
 "DecimalLatitude is greater than or equal to 55.1",
 "DecimalLatitude is less than or equal to 56.06"
 ]
}

 The dataset includes 232090 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000400-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48420/28653377
title: INForMHAA - Interpreter-Mediated Mental Health Act Assessments - AMHP Survey Results 27.03.2024

description: This dataset contains 132 anonymised survey responses from Approved Mental Health Professionals (AMHPs) documenting their experiences working with interpreters during Mental Health Act assessments. The responses include both quantitative data from structured questions and qualitative reflections from open-ended items. Presented in de-identified format, the dataset incorporates demographic information, service context details, and specific accounts of interpreter-mediated assessment experiences, providing both raw data and descriptive analysis.
DOI: 10.48420/28653377.v1
title: INForMHAA - Interpreter-Mediated Mental Health Act Assessments - AMHP Survey Results 27.03.2024

description: This dataset contains 132 anonymised survey responses from Approved Mental Health Professionals (AMHPs) documenting their experiences working with interpreters during Mental Health Act assessments. The responses include both quantitative data from structured questions and qualitative reflections from open-ended items. Presented in de-identified format, the dataset incorporates demographic information, service context details, and specific accounts of interpreter-mediated assessment experiences, providing both raw data and descriptive analysis.
DOI: 10.4119/unibi/2991373
title: Data: Testing and validation of the Transparency Of Robot Scale (TOROS)

description: These datasets result result from two experiments (referred to as Experiment 1 and Experiment 2) testing and validating a scale to assess a robot's perceived transparency. Two version of each dataset is provided: One with raw data (with participants meeting exclusion criteria), One with cleaned and enhanced data (removal of participants meeting exclusion criteria, creation of columns containing results based on rawdata).

The data are fully anonymous and therefore do not allow the identification of any participant.
DOI: 10.15468/dl.dafz8x
title: Occurrence Download

description: A dataset containing 26 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Europe",
 "Country is France",
 "Geometry POLYGON((-5.10048 47.72143,-1.13949 47.72143,-1.13949 49.03427,-5.10048 49.03427,-5.10048 47.72143))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Reynoutria japonica Houtt.",
 "Year 2024-2025"
 ]
}

 The dataset includes 26 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000424-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2x6ryk
title: Occurrence Download

description: A dataset containing 558 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "TaxonKey is Certhia familiaris brittanica Ridgway, 1882"
 ]
}

 The dataset includes 558 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000426-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.q26w53
title: Occurrence Download

description: A dataset containing 501 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Geometry POLYGON((49.99 24.03,111.86 24.03,111.86 52.97,49.99 52.97,49.99 24.03))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Pseudois nayaur (Hodgson, 1833)",
 "Year 1970-2025"
 ]
}

 The dataset includes 501 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000427-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15045543
title: Danish bat dataset - PAMGuard deep learning tutorial

description: Deep learning and bats - PAMGuard tutorial dataset

General Information

Overview

This is a dataset to be used in part two of the PAMGuard tutorial *Deep learning in PAMGuard*. The dataset is from an AudioMoth recording near a woodland in Denmark at 384kHz which will record the sounds (echolocation and social) of all species of bats in the area.

Year of data collection

2020 (AudioMoth), 2017-2020 (deep learning training data)

Geographic location of data collection:

Denmark

Funding sources that supported the collection of the data:

Carlsberg Semper Ardens Research Project and The Independent Research Fund Denmark

Recommended citation for this dataset:

Brinkløv S. M. M., Macaulay, J., Bergler, C., Tougaard, J., Beedholm, K., Elmeros, M., Madsen, P. T. M. (2023) Open-source workflow approaches to passive acoustic monitoring of bats. Methods in Ecology and Evolution Signe: https://doi.org/10.1111/2041-210X.14131

Data & File Overview

Description of dataset

These data include a small sample of acoustic recordings of bats at woodland edges made using an AudioMoth running firmware version 1.4.4 (Hill et al., 2018, openacousticdevices.org). 

The full dataset that these data are taken from can be found [here](https://doi.org/10.5061/dryad.4xgxd25fh) and the associated publication is [here](https://doi.org/10.1111/2041-210X.14131);

Files

The files are single channel .wav files made on an AudioMoth. These are 16 bit files but the recorder ADC was 12-bit and therefore the dynamic range is around 60 dB. The files are located in the wav folder. The deep learning model is located in the root directory. It is a .pk file which can be imported directly into PAMGuard. 

The folder structure is as follows:

```md├── wav                                            #Folder of .wav files│   ├── 20200817_010002.WAV│   ├── 20200817_010012.WAV │   └── '''├── README.md                                        #This readme file├── BAT_MULTI_JAMIE_5ms_256fft_10hop_MM_10_90_128_256_JIT_PITCHTIMECHANGE_AUG_V1.pk     #Bat deep learning model in PAMGuard compatible format.└── ```

Device locations

Odense (‘Skovsoen’): N55.3741; E10.3819

Sharing and access

These data are open source under Creative Commons Attribution 4.0 International. This means that these data can be used in other tutorials as long as the original authors are credited. Using these data for scientific purposes requires the permission of the authors.
DOI: 10.5281/zenodo.15045544
title: Danish bat dataset - PAMGuard deep learning tutorial

description: Deep learning and bats - PAMGuard tutorial dataset

General Information

Overview

This is a dataset to be used in part two of the PAMGuard tutorial *Deep learning in PAMGuard*. The dataset is from an AudioMoth recording near a woodland in Denmark at 384kHz which will record the sounds (echolocation and social) of all species of bats in the area.

Year of data collection

2020 (AudioMoth), 2017-2020 (deep learning training data)

Geographic location of data collection:

Denmark

Funding sources that supported the collection of the data:

Carlsberg Semper Ardens Research Project and The Independent Research Fund Denmark

Recommended citation for this dataset:

Brinkløv S. M. M., Macaulay, J., Bergler, C., Tougaard, J., Beedholm, K., Elmeros, M., Madsen, P. T. M. (2023) Open-source workflow approaches to passive acoustic monitoring of bats. Methods in Ecology and Evolution Signe: https://doi.org/10.1111/2041-210X.14131

Data & File Overview

Description of dataset

These data include a small sample of acoustic recordings of bats at woodland edges made using an AudioMoth running firmware version 1.4.4 (Hill et al., 2018, openacousticdevices.org). 

The full dataset that these data are taken from can be found [here](https://doi.org/10.5061/dryad.4xgxd25fh) and the associated publication is [here](https://doi.org/10.1111/2041-210X.14131);

Files

The files are single channel .wav files made on an AudioMoth. These are 16 bit files but the recorder ADC was 12-bit and therefore the dynamic range is around 60 dB. The files are located in the wav folder. The deep learning model is located in the root directory. It is a .pk file which can be imported directly into PAMGuard. 

The folder structure is as follows:

```md├── wav                                            #Folder of .wav files│   ├── 20200817_010002.WAV│   ├── 20200817_010012.WAV │   └── '''├── README.md                                        #This readme file├── BAT_MULTI_JAMIE_5ms_256fft_10hop_MM_10_90_128_256_JIT_PITCHTIMECHANGE_AUG_V1.pk     #Bat deep learning model in PAMGuard compatible format.└── ```

Device locations

Odense (‘Skovsoen’): N55.3741; E10.3819

Sharing and access

These data are open source under Creative Commons Attribution 4.0 International. This means that these data can be used in other tutorials as long as the original authors are credited. Using these data for scientific purposes requires the permission of the authors.
DOI: 10.5281/zenodo.10940426
title: Dataset of synthetic, maturation-informed magnetic resonance images of the human fetal brain

description: This dataset gathers synthetic-yet-highly-realistic T2-weighted magnetic resonance images (MRI) of the fetal brain based on the latest development of our prototype Fetal Brain magnetic resonance Acquisition Numerical phantom that now simulates local heterogeneities within white matter tissues throughout maturation (FaBiAN v2.0).This dataset is associated with the following paper:

- Lajous H. et al. (2024) A dataset of synthetic, maturation-informed magnetic resonance images of the human fetal brain. Submitted to Nature Scientific Data, Pre-print available https://doi.org/10.1101/2024.04.08.588566

We propose this unique, extensive fetal MRI dataset of simulated standard clinical fast spin echo sequences in both healthy and pathological neurodevelopmental trajectories to address data scarcity in this sensitive population, and therefore support the continuous endeavor of the community to develop advanced post-processing methods as well as cutting-edge artificial intelligence models. Automated brain tissue annotations of the two-dimensional, low-resolution series as well as super-resolution (SR) reconstructions of the fetal brain volumes are also included.

Work using any of these data should cite the following references:



Lajous, H. et al. A dataset of synthetic, maturation-informed magnetic resonance images of the human fetal brain. Submitted to Nature Scientific Data (2024), https://doi.org/10.1101/2024.04.08.588566

Lajous, H. et al. Dataset of synthetic, maturation-informed magnetic resonance images of the human fetal brain. Zenodo (2024). 10.5281/zenodo.10940427

Lajous, H., le Boeuf Fló, A., Esteban, O. & Bach Cuadra, M. Medical-Image-Analysis-Laboratory/FaBiAN: FaBiAN v2.0 (2.0). Zenodo (2023), 10.5281/zenodo.5471094


This work was supported by the Swiss National Science Foundation through grant 182602, and by the ProTechno Foundation. We acknowledge access to the facilities and expertise of the CIBM Center for Biomedical Imaging, a Swiss research center of excellence founded and supported by Lausanne University Hospital (CHUV), University of Lausanne (UNIL), Ecole Polytechnique Fédérale de Lausanne (EPFL), University of Geneva (UNIGE) and Geneva University Hospitals (HUG).

Medical Image Analysis Laboratory - Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland & CIBM Center for Biomedical Imaging. 2024.

Note: Terms of use for the original cohort (Fidon, L., Aertsen, M., Emam, D., et al. Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation. MICCAI, 2021) are for research and education purposes only.
DOI: 10.5281/zenodo.10940427
title: Dataset of synthetic, maturation-informed magnetic resonance images of the human fetal brain

description: This dataset gathers synthetic-yet-highly-realistic T2-weighted magnetic resonance images (MRI) of the fetal brain based on the latest development of our prototype Fetal Brain magnetic resonance Acquisition Numerical phantom that now simulates local heterogeneities within white matter tissues throughout maturation (FaBiAN v2.0).This dataset is associated with the following paper:

- Lajous H. et al. (2024) A dataset of synthetic, maturation-informed magnetic resonance images of the human fetal brain. Submitted to Nature Scientific Data, Pre-print available https://doi.org/10.1101/2024.04.08.588566

We propose this unique, extensive fetal MRI dataset of simulated standard clinical fast spin echo sequences in both healthy and pathological neurodevelopmental trajectories to address data scarcity in this sensitive population, and therefore support the continuous endeavor of the community to develop advanced post-processing methods as well as cutting-edge artificial intelligence models. Automated brain tissue annotations of the two-dimensional, low-resolution series as well as super-resolution (SR) reconstructions of the fetal brain volumes are also included.

Work using any of these data should cite the following references:



Lajous, H. et al. A dataset of synthetic, maturation-informed magnetic resonance images of the human fetal brain. Submitted to Nature Scientific Data (2024), https://doi.org/10.1101/2024.04.08.588566

Lajous, H. et al. Dataset of synthetic, maturation-informed magnetic resonance images of the human fetal brain. Zenodo (2024). 10.5281/zenodo.10940427

Lajous, H., le Boeuf Fló, A., Esteban, O. & Bach Cuadra, M. Medical-Image-Analysis-Laboratory/FaBiAN: FaBiAN v2.0 (2.0). Zenodo (2023), 10.5281/zenodo.5471094


This work was supported by the Swiss National Science Foundation through grant 182602, and by the ProTechno Foundation. We acknowledge access to the facilities and expertise of the CIBM Center for Biomedical Imaging, a Swiss research center of excellence founded and supported by Lausanne University Hospital (CHUV), University of Lausanne (UNIL), Ecole Polytechnique Fédérale de Lausanne (EPFL), University of Geneva (UNIGE) and Geneva University Hospitals (HUG).

Medical Image Analysis Laboratory - Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland & CIBM Center for Biomedical Imaging. 2024.

Note: Terms of use for the original cohort (Fidon, L., Aertsen, M., Emam, D., et al. Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation. MICCAI, 2021) are for research and education purposes only.
DOI: 10.15468/dl.85w8rx
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Specimen, Material sample)",
 "Country is China",
 "OccurrenceStatus is Present",
 "StateProvince is 浙江",
 "TaxonKey is Semiarundinaria Makino ex Nakai"
 ]
}

 The dataset includes 5 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000428-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.