cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.nf3jjh
title: Occurrence Download

description: A dataset containing 5814 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Polygonia c-album (Linnaeus, 1758)",
 "Year 1910-1955"
 ]
}

 The dataset includes 5814 records from 120 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0073311-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mrbbkf
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (India, Myanmar, Nepal, Bhutan, Bangladesh, China, Afghanistan, Pakistan)",
 "TaxonKey is Ypthima cantliei Norman, 1958"
 ]
}

 The dataset includes 4 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0073310-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.c62x4r
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (India, Nepal, Myanmar, Bhutan, China, Bangladesh, Afghanistan, Pakistan)",
 "TaxonKey is Ypthima singala Felder, 1868"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0073312-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qfehev
title: Occurrence Download

description: A dataset containing 1672 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Material sample)"
 ]
 }
 },
 "TaxonKey is Hylopsar cupreocauda (Hartlaub, 1857)",
 "Continent is one of (Africa)",
 "Year is greater than or equal to 1900"
 ]
}

 The dataset includes 1672 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0073313-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.68mp85
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (India, Myanmar, China, Pakistan, Nepal, Bangladesh, Afghanistan, Bhutan)",
 "TaxonKey is Ypthima bolanica Marshall, 1882"
 ]
}

 The dataset includes 4 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0073315-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mka97n
title: Occurrence Download

description: A dataset containing 104985 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Argiope bruennichi (Scopoli, 1772)",
 "Year 1980-2025"
 ]
}

 The dataset includes 104985 records from 1175 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0073290-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jebrax
title: Occurrence Download

description: A dataset containing 216 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Tomistoma schlegelii (Müller, 1838)"
 ]
}

 The dataset includes 216 records from 47 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000563-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14999917
title: Denoised data for the manuscript "Self-Supervised Coherence-Based Denoising of Cryoseismological Distributed Acoustic Sensing Data"

description: These datasets provide the necessary data to reproduce the results presented in the paper “Self-Supervised Coherence-Based Denoising of Cryoseismological Distributed Acoustic Sensing Data.” The corresponding code is available on GitHub, and the paper can be accessed via Authorea.

The data set "02_accumulation_denoisedDAS" contains the denoised data of the model J-invariant-cryo-acc.The data sets , "11_vanende_earth_denoisedDAS_image", "12_vanende_finetuned_cryo_denoisedDAS_image", "13_afk_denoisedDAS_image", "14_conventional_denoisedDAS_image", and "15_DASDL_denoisedDAS_image" contain the some denoised data files for the models "J-invariant-earth", "J-invariant-earth-cryo", ""AFK", "Conventional", and "DASDL" , needed for reproducing Figure 5, S7, and S8.

Abstract:

A major challenge in cryoseismology is that signals of interest are often buried within the high noise level emitted by a variety of environmental processes. Particular Distributed Acoustic Sensing (DAS) data often suffers from low signal-to-noise ratios (SNR) potentially resulting in a multitude of undetected events of interest, which further remain unanalyzed. To record seismicity, we deployed a DAS system on Rhône Glacier, Switzerland, using a 9 km long fiber-optic cable that covered the entire glacier, from its accumulation to its ablation zone. The highly active and dynamic cryospheric environment, in combination with poor coupling, resulted in DAS data characterized by a low SNR. Our objective is to develop and evaluate a method to effectively denoise this cryoseismological DAS dataset, while comparing our approach to state-of-the-art filtering and denoising methods. We propose the J-invariant-cryo denoiser, specifically trained on cryoseismological data and capable of separating incoherent environmental noise from temporally and spatially coherent signals of interest, based on a self-supervised J-invariant U-Net autoencoder. The method enhances inter-channel coherence, improves waveform similarity with co-located seismometers, and increases SNR. The comparison of different methods shows that our approach obtains the highest gain in SNR and highest similarity with co-located seismometers, while suffering from denoising artifacts in rare cases. The proposed denoiser has the potential to enhance the detection capabilities of events of interest in cryoseismological DAS data, hence to improve the understanding of processes within Alpine glaciers.
DOI: 10.5281/zenodo.14999918
title: Denoised data for the manuscript "Self-Supervised Coherence-Based Denoising of Cryoseismological Distributed Acoustic Sensing Data"

description: These datasets provide the necessary data to reproduce the results presented in the paper “Self-Supervised Coherence-Based Denoising of Cryoseismological Distributed Acoustic Sensing Data.” The corresponding code is available on GitHub, and the paper can be accessed via Authorea.

The data set "02_accumulation_denoisedDAS" contains the denoised data of the model J-invariant-cryo-acc.The data sets , "11_vanende_earth_denoisedDAS_image", "12_vanende_finetuned_cryo_denoisedDAS_image", "13_afk_denoisedDAS_image", "14_conventional_denoisedDAS_image", and "15_DASDL_denoisedDAS_image" contain the some denoised data files for the models "J-invariant-earth", "J-invariant-earth-cryo", ""AFK", "Conventional", and "DASDL" , needed for reproducing Figure 5, S7, and S8.

Abstract:

A major challenge in cryoseismology is that signals of interest are often buried within the high noise level emitted by a variety of environmental processes. Particular Distributed Acoustic Sensing (DAS) data often suffers from low signal-to-noise ratios (SNR) potentially resulting in a multitude of undetected events of interest, which further remain unanalyzed. To record seismicity, we deployed a DAS system on Rhône Glacier, Switzerland, using a 9 km long fiber-optic cable that covered the entire glacier, from its accumulation to its ablation zone. The highly active and dynamic cryospheric environment, in combination with poor coupling, resulted in DAS data characterized by a low SNR. Our objective is to develop and evaluate a method to effectively denoise this cryoseismological DAS dataset, while comparing our approach to state-of-the-art filtering and denoising methods. We propose the J-invariant-cryo denoiser, specifically trained on cryoseismological data and capable of separating incoherent environmental noise from temporally and spatially coherent signals of interest, based on a self-supervised J-invariant U-Net autoencoder. The method enhances inter-channel coherence, improves waveform similarity with co-located seismometers, and increases SNR. The comparison of different methods shows that our approach obtains the highest gain in SNR and highest similarity with co-located seismometers, while suffering from denoising artifacts in rare cases. The proposed denoiser has the potential to enhance the detection capabilities of events of interest in cryoseismological DAS data, hence to improve the understanding of processes within Alpine glaciers.
DOI: 10.5281/zenodo.4263841
title: Spectral Library of Impervious Urban Materials

description: The radiative response of impervious urban materials is a highly influential surface property, due to its impacts on the radiation balance of incoming and outgoing long- and short-wave fluxes. Information about the material composition can be determined with data resolved to the wavelength level. Spectral reflectance in the visible- to short-wave infrared (VIS-SWIR) region is widely used in remote sensing-based land cover classification and spectral long-wave infrared (LWIR) emissivity is required for the observation of surface temperatures. The Spectral Library of impervious Urban Materials (SLUM) available from the London Urban Micromet data Archive (LUMA) includes LWIR emissivity spectra of 74 samples of impervious surfaces derived using measurements made by a portable Fourier Transform InfraRed (FTIR) spectrometer and matching short-wave reflectance spectra observed for each urban sample.


The documentation (pdf) contains photos, meta information for all 74 samples and plots of the short-wave reflactance (300-2500 nm) and long-wave (8-14 um) emissivity spectra.


Data are provided in csv format for short-wave reflectance (LUMA_SLUM_SW.csv) and long-wave emissivity (LUMA_SLUM_LW.csv).


Further details (including integrated broadband values of emissivity and albedo), methods and data analysis are presented in


Kotthaus, S, TEL Smith, MJ Wooster, and CSB Grimmond 2014: Derivation of an urban materials spectral library through emittance and reflectance spectroscopy, ISPRS Journal of Photogrammetry and Remote Sensing, 94, 194–212. doi:10.1016/j.isprsjprs.2014.05.005


Please cite both this site and the paper above if you use this data
DOI: 10.5281/zenodo.4263842
title: Spectral Library of Impervious Urban Materials

description: The radiative response of impervious urban materials is a highly influential surface property, due to its impacts on the radiation balance of incoming and outgoing long- and short-wave fluxes. Information about the material composition can be determined with data resolved to the wavelength level. Spectral reflectance in the visible- to short-wave infrared (VIS-SWIR) region is widely used in remote sensing-based land cover classification and spectral long-wave infrared (LWIR) emissivity is required for the observation of surface temperatures. The Spectral Library of impervious Urban Materials (SLUM) available from the London Urban Micromet data Archive (LUMA) includes LWIR emissivity spectra of 74 samples of impervious surfaces derived using measurements made by a portable Fourier Transform InfraRed (FTIR) spectrometer and matching short-wave reflectance spectra observed for each urban sample.


The documentation (pdf) contains photos, meta information for all 74 samples and plots of the short-wave reflactance (300-2500 nm) and long-wave (8-14 um) emissivity spectra.


Data are provided in csv format for short-wave reflectance (LUMA_SLUM_SW.csv) and long-wave emissivity (LUMA_SLUM_LW.csv).


Further details (including integrated broadband values of emissivity and albedo), methods and data analysis are presented in


Kotthaus, S, TEL Smith, MJ Wooster, and CSB Grimmond 2014: Derivation of an urban materials spectral library through emittance and reflectance spectroscopy, ISPRS Journal of Photogrammetry and Remote Sensing, 94, 194–212. doi:10.1016/j.isprsjprs.2014.05.005


Please cite both this site and the paper above if you use this data
DOI: 10.25413/sun.28564982
title: iNaturalist as a tool to supplement alien species monitoring: a South African marine protected areas perspective

description: Concerns and limitations relating to data quality, reliability and accuracy hamper the use of citizen science initiatives in research and conservation. Valued for their cost-effective and large data acquisition potential, citizen science platforms such as iNaturalist have been highlighted as beneficial tools to supplement monitoring using traditional data sources. However, intrinsic uncertainties in unverified observations stem from the nature of species being identified, the quality of uploaded media and georeferencing; these factors can limit the value of the data as they can result in inaccurate records. Verification of data prior to use is critical. This process can, however, be laborious and time-consuming, with bias associated with the individual responsible for the task. To address this challenge this study developed a protocol for assigning confidence in iNaturalist observations, using marine alien and cryptogenic species observations from South Africa as a case study. A positive relationship was found between the accuracy of observations and confidence score. The inherent data quality assessment in iNaturalist, termed quality grade, was found to be an inadequate proxy for accuracy. The results of this study highlight the importance of the expert verification phase when using citizen science data. The confidence score facilitates a streamlined approach to the verification process by reducing the time taken to validate records, while assessing the three levels of uncertainty within observations and reducing researcher bias. It is recommended that this confidence score be used as an essential tool when using citizen science derived data.
DOI: 10.25413/sun.28564982.v1
title: iNaturalist as a tool to supplement alien species monitoring: a South African marine protected areas perspective

description: Concerns and limitations relating to data quality, reliability and accuracy hamper the use of citizen science initiatives in research and conservation. Valued for their cost-effective and large data acquisition potential, citizen science platforms such as iNaturalist have been highlighted as beneficial tools to supplement monitoring using traditional data sources. However, intrinsic uncertainties in unverified observations stem from the nature of species being identified, the quality of uploaded media and georeferencing; these factors can limit the value of the data as they can result in inaccurate records. Verification of data prior to use is critical. This process can, however, be laborious and time-consuming, with bias associated with the individual responsible for the task. To address this challenge this study developed a protocol for assigning confidence in iNaturalist observations, using marine alien and cryptogenic species observations from South Africa as a case study. A positive relationship was found between the accuracy of observations and confidence score. The inherent data quality assessment in iNaturalist, termed quality grade, was found to be an inadequate proxy for accuracy. The results of this study highlight the importance of the expert verification phase when using citizen science data. The confidence score facilitates a streamlined approach to the verification process by reducing the time taken to validate records, while assessing the three levels of uncertainty within observations and reducing researcher bias. It is recommended that this confidence score be used as an essential tool when using citizen science derived data.
DOI: 10.5281/zenodo.13740672
title: A method for conveying confidence in iNaturalist observations: a case study using non-native marine species

description: This data and code supports the paper Ackland et al.   A method for conveying confidence in iNaturalist observations: a case study using non-native marine species
DOI: 10.15468/dl.tubkqz
title: Occurrence Download

description: A dataset containing 345 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Morocco",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Uromastyx Merrem, 1820"
 ]
}

 The dataset includes 345 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000564-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14998652
title: Dataset for the manuscript "Self-Supervised Coherence-Based Denoising of Cryoseismological Distributed Acoustic Sensing Data"

description: The dataset "raw_DAS" contains cryoseismological distributed acoustic sensing (DAS) data recorded in July 2020 on Rhône Glacier, Switzerland.

The dataset "training_data" contains raw and preprocessed seismometer recordings used as training data, also recorded in July 2020 on Rhône Glacier, Switzerland.

These datasets provide the necessary data to reproduce the results presented in the paper “Self-Supervised Coherence-Based Denoising of Cryoseismological Distributed Acoustic Sensing Data.” The corresponding code is available on GitHub, and the paper can be accessed via Authorea.

 

Abstract

A major challenge in cryoseismology is that signals of interest are often buried within the high noise level emitted by a variety of environmental processes. Particular Distributed Acoustic Sensing (DAS) data often suffers from low signal-to-noise ratios (SNR) potentially resulting in a multitude of undetected events of interest, which further remain unanalyzed. To record seismicity, we deployed a DAS system on Rhône Glacier, Switzerland, using a 9 km long fiber-optic cable that covered the entire glacier, from its accumulation to its ablation zone. The highly active and dynamic cryospheric environment, in combination with poor coupling, resulted in DAS data characterized by a low SNR. Our objective is to develop and evaluate a method to effectively denoise this cryoseismological DAS dataset, while comparing our approach to state-of-the-art filtering and denoising methods. We propose the J-invariant-cryo denoiser, specifically trained on cryoseismological data and capable of separating incoherent environmental noise from temporally and spatially coherent signals of interest, based on a self-supervised J-invariant U-Net autoencoder. The method enhances inter-channel coherence, improves waveform similarity with co-located seismometers, and increases SNR. The comparison of different methods shows that our approach obtains the highest gain in SNR and highest similarity with co-located seismometers, while suffering from denoising artifacts in rare cases. The proposed denoiser has the potential to enhance the detection capabilities of events of interest in cryoseismological DAS data, hence to improve the understanding of processes within Alpine glaciers.
DOI: 10.5281/zenodo.14998653
title: Dataset for the manuscript "Self-Supervised Coherence-Based Denoising of Cryoseismological Distributed Acoustic Sensing Data"

description: The dataset "raw_DAS" contains cryoseismological distributed acoustic sensing (DAS) data recorded in July 2020 on Rhône Glacier, Switzerland.

The dataset "training_data" contains raw and preprocessed seismometer recordings used as training data, also recorded in July 2020 on Rhône Glacier, Switzerland.

These datasets provide the necessary data to reproduce the results presented in the paper “Self-Supervised Coherence-Based Denoising of Cryoseismological Distributed Acoustic Sensing Data.” The corresponding code is available on GitHub, and the paper can be accessed via Authorea.

 

Abstract

A major challenge in cryoseismology is that signals of interest are often buried within the high noise level emitted by a variety of environmental processes. Particular Distributed Acoustic Sensing (DAS) data often suffers from low signal-to-noise ratios (SNR) potentially resulting in a multitude of undetected events of interest, which further remain unanalyzed. To record seismicity, we deployed a DAS system on Rhône Glacier, Switzerland, using a 9 km long fiber-optic cable that covered the entire glacier, from its accumulation to its ablation zone. The highly active and dynamic cryospheric environment, in combination with poor coupling, resulted in DAS data characterized by a low SNR. Our objective is to develop and evaluate a method to effectively denoise this cryoseismological DAS dataset, while comparing our approach to state-of-the-art filtering and denoising methods. We propose the J-invariant-cryo denoiser, specifically trained on cryoseismological data and capable of separating incoherent environmental noise from temporally and spatially coherent signals of interest, based on a self-supervised J-invariant U-Net autoencoder. The method enhances inter-channel coherence, improves waveform similarity with co-located seismometers, and increases SNR. The comparison of different methods shows that our approach obtains the highest gain in SNR and highest similarity with co-located seismometers, while suffering from denoising artifacts in rare cases. The proposed denoiser has the potential to enhance the detection capabilities of events of interest in cryoseismological DAS data, hence to improve the understanding of processes within Alpine glaciers.
DOI: 10.5281/zenodo.2581484
title: 2010\London\AMDAR\

description: 2010\London\AMDAR\
Type: Aircraft data landing/taking off around London
Files: Hourly
Zipped by Month
AMYYYYDDD_HH.txt-


AM-amdar
YYYY - year
DDD- day of year
HH hour
example: AM2010365_23.txt
DOI: 10.5281/zenodo.2581485
title: 2010\London\AMDAR\

description: 2010\London\AMDAR\
Type: Aircraft data landing/taking off around London
Files: Hourly
Zipped by Month
AMYYYYDDD_HH.txt-


AM-amdar
YYYY - year
DDD- day of year
HH hour
example: AM2010365_23.txt
DOI: 10.15468/dl.ju8ab7
title: Occurrence Download

description: A dataset containing 90 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Platystele oxyglossa (Schltr.) Garay"
 ]
}

 The dataset includes 90 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000565-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.y2g4t4
title: Occurrence Download

description: A dataset containing 288 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Zamiaceae"
 ]
}

 The dataset includes 288 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000566-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.