cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.wy42pu
title: Occurrence Download

description: A dataset containing 95 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-14.6582 23.19759,12.68555 23.19759,12.68555 35.73957,-14.6582 35.73957,-14.6582 23.19759))",
 "HasGeospatialIssue is false",
 "TaxonKey is Cirsium Mill."
 ]
}

 The dataset includes 95 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0043473-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.x8ktph
title: Occurrence Download

description: A dataset containing 100 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Coronilla elegans Pancic",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 100 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0079610-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.adkb6t
title: Occurrence Download

description: A dataset containing 1555422 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((2.43896 51.96533,67.2583 53.72314,66.99463 72.8833,3.01025 71.30127,2.43896 51.96533))",
 "TaxonKey is Agaricales",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1555422 records from 153 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015898-190621201848488/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ak3cch
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Dendrophthora coronata Canelón, S.M.Niño, Dorr & Caraballo"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0154462-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nprjqn
title: Occurrence Download

description: A dataset containing 372634 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Quelea quelea (Linnaeus, 1758)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
}

 The dataset includes 372634 records from 100 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002792-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7giwut
title: Occurrence Download

description: A dataset containing 6457 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Euthamia graminifolia (L.) Nutt."
 ]
}

 The dataset includes 6457 records from 133 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015909-190621201848488/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w6ndsv
title: Occurrence Download

description: A dataset containing 554 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is EDP Baixo Sabor: Construction and Reservoir Filling Phases - Riparian Birds, priority conservation species: fixed points & complementary observations - [2010-2014]"
 ]
}

 The dataset includes 554 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0230049-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tn9qvn
title: Occurrence Download

description: A dataset containing 11 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Coprosma benefica W.R.B.Oliv."
 ]
}

 The dataset includes 11 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013955-240314170635999/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.1bvtst
title: Occurrence Download

description: A dataset containing 5352 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Bidens subalternans DC."
 ]
}

 The dataset includes 5352 records from 179 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015895-190621201848488/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xaee6u
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Peperomia espinosae Yunck."
 ]
}

 The dataset includes 8 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008738-210819072339941/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8u2frm
title: Occurrence Download

description: A dataset containing 89 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Galium palaeoitalicum Ehrend."
 ]
}

 The dataset includes 89 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010328-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gcr7tm
title: Occurrence Download

description: A dataset containing 112 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Eugenia gongylocarpa M.L.Kawas. & B.Holst"
 ]
}

 The dataset includes 112 records from 29 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0154464-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nl06m4
title: Occurrence Download

description: A dataset containing 398426 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cirsium vulgare (Savi) Ten."
 ]
}

 The dataset includes 398426 records from 763 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015903-190621201848488/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.28812431
title: <b>Coexistence patterns of biocrust-vascular plant in the Loess Plateau</b>

description: The Loess Plateau is the most typical loess platform worldwide and has experienced heavy restoration since 1999, costing hundreds of billions of Chinese Yuan. However, there has been a long-lasting and huge controversy about how to restore and manage different ecosystem types in the Loess Plateau. A primary reason is the lack of a clear idea of how major biological components – biocrusts (a mixture of soil particles, cyanobacteria, fungi, lichen, and moss), grasses, and shrubs coexist together therein and of system equilibrium behavior, which will significantly affect our understanding about restoration efforts. To that end, this study integrated field surveys, literature review, and environmental data analysis to identify determining factors and coexistence patterns of biocrust and vascular plants (grass and shrub) in the Loess Plateau. We found that precipitation, surface temperature, and solar radiation were the most important determining factors of biocrust, grass, and shrub cover in the Loess Plateau. Three coexistence patterns were identified based on these key environmental factors, namely a high biocrust-grass mixed state under high annual rainfall and surface temperatures of 10-12°C, a biocrust-dominated state under low rainfall conditions, and a low biocrust-grass mixed state under low surface temperature and strong solar radiation situations. More importantly, we found that these three coexistence patterns appeared to be alternative stable states along the annual precipitation gradient. This study, for the first time, discovered coexistence patterns of biocrust-vascular plants in the Loess Plateau and can guide future restoration and conservation in the region, which is crucial for achieving sustainable development and ecological safety in North China.
DOI: 10.15468/dl.c4ktjd
title: Occurrence Download

description: A dataset containing 57112 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Tardigrada"
 ]
}

 The dataset includes 57112 records from 276 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010640-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gy5fvd
title: Occurrence Download

description: A dataset containing 428 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Dactylorhiza baltica (Klinge) N.I.Orlova",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 428 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010643-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.27324882
title: Code sar

description: r code
DOI: 10.5281/zenodo.11380834
title: Helsinki Speech Challenge 2024 open audio dataset

description: Dataset

This training dataset is originally designed for the Helsinki Speech Challenge 2024 (HSC2024). While it was created with this challenge in mind, its applications extend far beyond, making it a valuable resource for developing and testing audio algorithms across diverse uses.

Our dataset features clean speech samples generated by OpenAI's text-to-speech model, paired with corresponding recorded signals. These recorded signals are purposefully distorted by real-world effects such as filtering and reverb, offering a realistic testing ground for your audio processing algorithms.

To ensure ease of use, the audio samples are organized into 10 separate zip files, each dedicated to a specific task and level of complexity. Most zip files include two folders clean and recorded: one containing clean audio data and the other housing the corresponding recorded (distorted) data. However, note that folders Task_3_Level_1 and Task_3_Level_2 only include recorded data, as their clean counterparts are identical to those in Task_2_Level_2 and Task_2_Level_3, respectively. Additionally, each zip file includes a .txt file with the original text samples associated with the audio clips.Update 29th October: We have now also added the test data that was used for evaluation in the challenge with similar structure to the rest of the data, contained in the zip files starting with "Test".

Additionally, there is folder Impulse_Responses, which contains a clean sine sweep signal and short and a long white noise signal with recorded counterparts. To help you get started, we've also provided an example folder containing samples from each task and level, giving you a comprehensive overview of the dataset's scope and variety.The dataset also contains a python script evaluate.py. This can be used to evaluate the quality of audio files using the Mozilla Deepspeech speech recognition model. For more details on this script, see the more detailed description of the data challenge either on the website below or on arXiv https://arxiv.org/abs/2406.04123.Important dates:



Data Challenge Launch: 10. June 2024.

Sign-up deadline: 1. September 2024 (if you missed this deadline and wish to participate in the challenge, please send us an email).

Submission deadline: 6. October 2024. We realize this deadline is a bit optimistic, but we humbly ask participants to try to make this deadline.

Results are published: 4. November.

Inverse days: 10.-13. December in Oulu, Finland.


Here is a link to the official webpage of the HSC2024: https://blogs.helsinki.fi/helsinki-speech-challenge/Contact Email: hsc2024@helsinki.fi
DOI: 10.5281/zenodo.11380835
title: Helsinki Speech Challenge 2024 open audio dataset

description: Dataset

This training dataset is originally designed for the Helsinki Speech Challenge 2024 (HSC2024). While it was created with this challenge in mind, its applications extend far beyond, making it a valuable resource for developing and testing audio algorithms across diverse uses.

Our dataset features clean speech samples generated by OpenAI's text-to-speech model, paired with corresponding recorded signals. These recorded signals are purposefully distorted by real-world effects such as filtering and reverb, offering a realistic testing ground for your audio processing algorithms.

To ensure ease of use, the audio samples are organized into 10 separate zip files, each dedicated to a specific task and level of complexity. Most zip files include two folders clean and recorded: one containing clean audio data and the other housing the corresponding recorded (distorted) data. However, note that folders Task_3_Level_1 and Task_3_Level_2 only include recorded data, as their clean counterparts are identical to those in Task_2_Level_2 and Task_2_Level_3, respectively. Additionally, each zip file includes a .txt file with the original text samples associated with the audio clips.

Additionally, there is folder Impulse_Responses, which contains a clean sine sweep signal and short and a long white noise signal with recorded counterparts. To help you get started, we've also provided an example folder containing samples from each task and level, giving you a comprehensive overview of the dataset's scope and variety.The dataset also contains a python script evaluate.py. This can be used to evaluate the quality of audio files using the Mozilla Deepspeech speech recognition model. For more details on this script, see the more detailed description of the data challenge either on the website below or on arXiv https://arxiv.org/abs/2406.04123.Important dates:



Data Challenge Launch: 10. June 2024.

Sign-up deadline: 1. September 2024 (if you missed this deadline and wish to participate in the challenge, please send us an email).

Submission deadline: 6. October 2024. We realize this deadline is a bit optimistic, but we humbly ask participants to try to make this deadline.

Results are published: 4. November.

Inverse days: 10.-13. December in Oulu, Finland.


Here is a link to the official webpage of the HSC2024: https://blogs.helsinki.fi/helsinki-speech-challenge/Contact Email: hsc2024@helsinki.fi
DOI: 10.5281/zenodo.14007505
title: Helsinki Speech Challenge 2024 open audio dataset

description: Dataset

This training dataset is originally designed for the Helsinki Speech Challenge 2024 (HSC2024). While it was created with this challenge in mind, its applications extend far beyond, making it a valuable resource for developing and testing audio algorithms across diverse uses.

Our dataset features clean speech samples generated by OpenAI's text-to-speech model, paired with corresponding recorded signals. These recorded signals are purposefully distorted by real-world effects such as filtering and reverb, offering a realistic testing ground for your audio processing algorithms.

To ensure ease of use, the audio samples are organized into 10 separate zip files, each dedicated to a specific task and level of complexity. Most zip files include two folders clean and recorded: one containing clean audio data and the other housing the corresponding recorded (distorted) data. However, note that folders Task_3_Level_1 and Task_3_Level_2 only include recorded data, as their clean counterparts are identical to those in Task_2_Level_2 and Task_2_Level_3, respectively. Additionally, each zip file includes a .txt file with the original text samples associated with the audio clips.Update 29th October: We have now also added the test data that was used for evaluation in the challenge with similar structure to the rest of the data, contained in the zip files starting with "Test".

Additionally, there is folder Impulse_Responses, which contains a clean sine sweep signal and short and a long white noise signal with recorded counterparts. To help you get started, we've also provided an example folder containing samples from each task and level, giving you a comprehensive overview of the dataset's scope and variety.The dataset also contains a python script evaluate.py. This can be used to evaluate the quality of audio files using the Mozilla Deepspeech speech recognition model. For more details on this script, see the more detailed description of the data challenge either on the website below or on arXiv https://arxiv.org/abs/2406.04123.Important dates:



Data Challenge Launch: 10. June 2024.

Sign-up deadline: 1. September 2024 (if you missed this deadline and wish to participate in the challenge, please send us an email).

Submission deadline: 6. October 2024. We realize this deadline is a bit optimistic, but we humbly ask participants to try to make this deadline.

Results are published: 4. November.

Inverse days: 10.-13. December in Oulu, Finland.


Here is a link to the official webpage of the HSC2024: https://blogs.helsinki.fi/helsinki-speech-challenge/Contact Email: hsc2024@helsinki.fi
DOI: 10.15468/dl.3ussbc
title: Occurrence Download

description: A dataset containing 1293 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Issue is one of (Continent derived from coordinates, Continent derived from country)",
 "TaxonKey is Sporophila hypochroma Todd, 1915"
 ]
}

 The dataset includes 1293 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010645-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qp9vxq
title: Occurrence Download

description: A dataset containing 2775 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Catálogo digital y clave ilustrada de los mosquitos (Diptera: Culicidae) de las zonas áridas de los estados de Nuevo León y Tamaulipas, México"
 ]
}

 The dataset includes 2775 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010646-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.anakhn
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Dactylorhiza ebudensis (Wief. ex R.M.Bateman & Denholm) P.Delforge",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010647-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8m7rqu
title: Occurrence Download

description: A dataset listing the 4030839 species recorded in GBIF matching the query:
{ }

 The dataset's 4030839 records were derived from 77171 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010620-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.d4sd2w
title: Occurrence Download

description: A dataset containing 4804 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is MEX.26_1",
 "OccurrenceStatus is Present",
 "TaxonKey is Pandionidae"
 ]
}

 The dataset includes 4804 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010649-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13991911
title: MULTIPLIERS_WP5_Science learning project on AMR_UAB_Public data_v1

description: The datasets contain the following data related to the science learning project on ARM: 



Summary of transcripts from interviews with OSC members, and student groups (Pseudo-/Anonymised).
DOI: 10.5281/zenodo.13991912
title: MULTIPLIERS_WP5_Science learning project on AMR_UAB_Public data_v1

description: The datasets contain the following data related to the science learning project on ARM: 



Summary of transcripts from interviews with OSC members, and student groups (Pseudo-/Anonymised).
DOI: 10.5281/zenodo.13619469
title: Total Solar Irradiance (TSI) in the satellite era: Multiple new or updated satellite TSI composites and related time series
title: Supporting Data for "Multiple new or updated satellite Total Solar Irradiance (TSI) composites (1978-2023)"

description: Supporting dataset to accompany R. Connolly et al. (2024)

Associated journal article:

R. Connolly, W. Soon, M. Connolly, R.G. Cionco, A.G. Elias, G.W. Henry, N. Scafetta, and V.M. Velasco Herrera (2024). Multiple new or updated satellite Total Solar Irradiance (TSI) composites (1978-2023). The Astrophysical Journal. 975 (1), 102. https://doi.org/10.3847/1538-4357/ad7794

 

Description of contents:

An Excel spreadsheet with 25 tabs. All of the TSI composite time series, the proxy-based models used for comparison, and the data used to generate the figures and tables are provided in this sheet. The first tab describes the contents of the remaining tabs.

 

System requirements:

MS Excel or another program that can read .xlsx files.

 

Additional comments:

Version 1.0 is a mirror archive of the original Supporting Data for the journal article described above. Updates to this dataset will be provided here at https://doi.org/10.5281/zenodo.13619470 or on the CERES-Science website at https://www.ceres-science.com/portfolio-collections/solar-activity/total-solar-irradiance-in-the-satellite-era. 

 

Citation request:

We ask that users of this dataset include an appropriate citation to the R. Connolly et al. (2024, The Astrophysical Journal) article in any publications that make use of the dataset. If possible, we encourage including a direct citation to this dataset as well as to the article. We recommend including the version number and/or download date if you are using an update from the original dataset.

 

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