cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh0205088.10fu
title: SH0205088.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0205089.10fu
title: SH0205089.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0205090.10fu
title: SH0205090.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0205091.10fu
title: SH0205091.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0205092.10fu
title: SH0205092.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.5281/zenodo.15173239
title: Tarmachan Automatic Weather Station (National Trust for Scotland/University of Dundee)

description: Mountain weather station on the south-facing slopes of Meall nan Tarmachan on the National Trust for Scotland's National Nature Reserve (NNR).  The site is at 710 m above sea level, overlooking Loch Tay.  Snowcover has historically been an important part of the climate of the site, but in recent years has become increasingly transient.

The NNR is the most important in Scotland for its arctic alpine flora, we are noticing changes in species distribution and to habitats indicative of the effects of climate change.  Hence the establishment of this weather station to allow changes in climate at altitude to be monitored.  This site installed May 2018 ~20 m beyond the headwall of a disused quarry.  Winds may cause undercatch of rainfall (though the gauge is of an aerodynamic design) and more particularly snow.  Recorded wind speeds may underestimate winds generally around the mountain owing to effects of the quarry wall.

An earlier site was operated nearby in the early 2000s but sadly data have been lost.

Data are recorded on a Campbell Scientific CR1000 data logger, running with 10 s scan rate and 15 min logging interval.

Snow depths may be inferred by examining TCDT (temperature-corrected depth to target) data - obtained from a SR50A sensor on an arm c. 2.3 m above ground.  These are available only for limited periods in 2019 and again winter 2021/22, and are now discontinued.  However, snow cover can be inferred by examining the differential between air temperature and ground temperature: the ground sensor is insulated when snow covers the ground.

Sensor details: see metdata

Real-time data are displayed graphically at https://hydro-data.dundee.ac.uk/tarmachan (no downloads)
DOI: 10.5281/zenodo.10797154
title: Tarmachan Automatic Weather Station (National Trust for Scotland/University of Dundee)

description: Mountain weather station on the south-facing slopes of Meall nan Tarmachan on the National Trust for Scotland's National Nature Reserve (NNR).  The site is at 710 m above sea level, overlooking Loch Tay.  Snowcover has historically been an important part of the climate of the site, but in recent years has become increasingly transient.

The NNR is the most important in Scotland for its arctic alpine flora, we are noticing changes in species distribution and to habitats indicative of the effects of climate change.  Hence the establishment of this weather station to allow changes in climate at altitude to be monitored.  This site installed May 2018 ~20 m beyond the headwall of a disused quarry.  Winds may cause undercatch of rainfall (though the gauge is of an aerodynamic design) and more particularly snow.  Recorded wind speeds may underestimate winds generally around the mountain owing to effects of the quarry wall.

An earlier site was operated nearby in the early 2000s but sadly data have been lost.

Data are recorded on a Campbell Scientific CR1000 data logger, running with 10 s scan rate and 15 min logging interval.

Snow depths may be inferred by examining TCDT (temperature-corrected depth to target) data - obtained from a SR50A sensor on an arm c. 2.3 m above ground.  These are available only for limited periods in 2019 and again winter 2021/22, and are now discontinued.  However, snow cover can be inferred by examining the differential between air temperature and ground temperature: the ground sensor is insulated when snow covers the ground.

Sensor details: see metdata

Real-time data are displayed graphically at https://hydro-data.dundee.ac.uk/tarmachan (no downloads)
DOI: 10.5281/zenodo.10797155
title: Tarmachan Automatic Weather Station (National Trust for Scotland/University of Dundee)

description: Mountain weather station on the south-facing slopes of Meall nan Tarmachan on the National Trust for Scotland's National Nature Reserve (NNR).  The site is at 710 m above sea level, overlooking Loch Tay.  Snowcover has historically been an important part of the climate of the site, but in recent years has become increasingly transient.

The NNR is the most important in Scotland for its arctic alpine flora, we are noticing changes in species distribution and to habitats indicative of the effects of climate change.  Hence the establishment of this weather station to allow changes in climate at altitude to be monitored.  This site installed May 2018 ~20 m beyond the headwall of a disused quarry.  Winds may cause undercatch of rainfall (though the gauge is of an aerodynamic design) and more particularly snow.  Recorded wind speeds may underestimate winds generally around the mountain owing to effects of the quarry wall.

An earlier site was operated nearby in the early 2000s but sadly data have been lost.

Data are recorded on a Campbell Scientific CR1000 data logger, running with 10 s scan rate and 15 min logging interval.

Snow depths may be inferred by examining TCDT (temperature-corrected depth to target) data - obtained from a SR50A sensor on an arm c. 2.3 m above ground.  These are available only for limited periods in 2019 and again winter 2021/22, and are now discontinued.  However, snow cover can be inferred by examining the differential between air temperature and ground temperature: the ground sensor is insulated when snow covers the ground.

Sensor details: see metdata

Real-time data are displayed graphically at https://hydro-data.dundee.ac.uk/tarmachan (no downloads)
DOI: 10.13145/bacdive7204.20241212.9.2
title: Microvirga aerophila Weon et al. 2010

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.5281/zenodo.15173252
title: US Veterinary Orthopedic Implants Market 2024 To 2033

description: US Veterinary Orthopedic Implants Market Size, Trends and Insights By Product (Tibial Plateau Levelling Osteotomy, Advanced Locking Plate System, Total Knee Replacement Implants, Others), By End User (Hospitals, Clinics, Others), By Animal (Dogs, Cats, Pigs, Sheep, Horse, Others), and By Region - Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2024–2033.

Reports Description

As per the current market research conducted by the CMI Team, the US Veterinary Orthopedic Implants Market is expected to record a CAGR of 4.4% from 2023 to 2032. In 2023, the market size is projected to reach a valuation of USD 59.4 Million. By 2032, the valuation is anticipated to reach USD 87.5 Million.

The US Veterinary Orthopedic Implants market encompasses the manufacturing, distribution, and utilization of orthopedic devices for surgical interventions in animals. Fueled by increasing pet ownership, technological innovations, and a growing aging pet population, the market addresses conditions such as fractures and joint disorders in pets.

Key players, including B. Braun Vet Care, BioMedtrix, and DePuy Synthes Vet, drive the industry through continuous research and development, introducing advanced implant materials and surgical techniques. The market’s dynamic landscape reflects a commitment to enhancing animal healthcare outcomes across diverse species in the US.

For more information, DOWNLOAD FREE SAMPLE Now at https://www.custommarketinsights.com/request-for-free-sample/?reportid=39536
DOI: 10.5281/zenodo.15173253
title: US Veterinary Orthopedic Implants Market 2024 To 2033

description: US Veterinary Orthopedic Implants Market Size, Trends and Insights By Product (Tibial Plateau Levelling Osteotomy, Advanced Locking Plate System, Total Knee Replacement Implants, Others), By End User (Hospitals, Clinics, Others), By Animal (Dogs, Cats, Pigs, Sheep, Horse, Others), and By Region - Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2024–2033.

Reports Description

As per the current market research conducted by the CMI Team, the US Veterinary Orthopedic Implants Market is expected to record a CAGR of 4.4% from 2023 to 2032. In 2023, the market size is projected to reach a valuation of USD 59.4 Million. By 2032, the valuation is anticipated to reach USD 87.5 Million.

The US Veterinary Orthopedic Implants market encompasses the manufacturing, distribution, and utilization of orthopedic devices for surgical interventions in animals. Fueled by increasing pet ownership, technological innovations, and a growing aging pet population, the market addresses conditions such as fractures and joint disorders in pets.

Key players, including B. Braun Vet Care, BioMedtrix, and DePuy Synthes Vet, drive the industry through continuous research and development, introducing advanced implant materials and surgical techniques. The market’s dynamic landscape reflects a commitment to enhancing animal healthcare outcomes across diverse species in the US.

For more information, DOWNLOAD FREE SAMPLE Now at https://www.custommarketinsights.com/request-for-free-sample/?reportid=39536
DOI: 10.13145/bacdive7205.20241212.9.2
title: Methylohalobius crimeensis Heyer et al. 2005

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive7206.20241212.9.2
title: Methylotuvimicrobium alcaliphilum (Kalyuzhnaya et al. 2008) Orata et al. 2019

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive7209.20241212.9.2
title: Methylomonas methanica (ex Söhngen 1906) Whittenbury and Krieg 1984

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive7210.20241212.9.2
title: Methylosarcina fibrata Wise et al. 2001

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.6084/m9.figshare.29413010
title: Data base vertical jump and recovery volley

description: ----
DOI: 10.6084/m9.figshare.29413010.v1
title: Data base vertical jump and recovery volley

description: ----
DOI: 10.5281/zenodo.10944075
title: National estimates of U.S. storm surge extremes and underlying long-term trends (1950-2020) using spatiotemporal non-stationary Bayesian Hierarchical Modeling

description: Version 2 contains two updated files from version 1:

These files contain updated versions for these two files:



Posterior-median estimates of GEV parameters (shape, scale and location) and associated credible interval (CI) values for 208 tide gauge.

Posterior-median estimates of GEV parameters (shape, scale and location) and associated credible interval (CI) values for the entire U.S. coastlines


NOTE: all data and associated articles (listed below) require proper acknowlement, citation and referencing of all data when directly or indirectly using any archived file listed above.

Associated publications:

Morim, J., Wahl, T., Rasmussen, D.J. et al. Observations reveal changing coastal storm extremes around the United States. Nature Climate Change (2025). https://doi.org/10.1038/s41558-025-02315-z

Associated model:

Calafat, F. M. (2024). BAYEX: Spatiotemporal Bayesian hierarchical modeling of extremes with max-stable processes (v3.0). Zenodo. https://doi.org/10.5281/zenodo.10967174

The full reanalysis data with predicted probabilistic estimates of surge and sea level extremes at ungauged sites are available here:

Morim, J. (2025). Observation-based probabilistic reanalysis of storm surge and sea level extremes for United States., [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10967174 

Media:

https://cpree.princeton.edu/news/2025/researchers-uncover-clearer-patterns-extreme-storm-surge-trends-amid-rising-sea-levels
DOI: 10.5281/zenodo.15711412
title: National estimates of U.S. storm surge extremes and underlying long-term trends (1950-2020) using spatiotemporal non-stationary Bayesian Hierarchical Modeling

description: Version 2 contains two updated files from version 1:

These files contain updated versions for these two files:



Posterior-median estimates of GEV parameters (shape, scale and location) and associated credible interval (CI) values for 208 tide gauge.

Posterior-median estimates of GEV parameters (shape, scale and location) and associated credible interval (CI) values for the entire U.S. coastlines


NOTE: all data and associated articles (listed below) require proper acknowlement, citation and referencing of all data when directly or indirectly using any archived file listed above.

Associated publications:

Morim, J., Wahl, T., Rasmussen, D.J. et al. Observations reveal changing coastal storm extremes around the United States. Nature Climate Change (2025). https://doi.org/10.1038/s41558-025-02315-z

Associated model:

Calafat, F. M. (2024). BAYEX: Spatiotemporal Bayesian hierarchical modeling of extremes with max-stable processes (v3.0). Zenodo. https://doi.org/10.5281/zenodo.10967174

The full reanalysis data with predicted probabilistic estimates of surge and sea level extremes at ungauged sites are available here:

Morim, J. (2025). Observation-based probabilistic reanalysis of storm surge and sea level extremes for United States., [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10967174 

Media:

https://cpree.princeton.edu/news/2025/researchers-uncover-clearer-patterns-extreme-storm-surge-trends-amid-rising-sea-levels
DOI: 10.5281/zenodo.8223023
title: UKCP18 RCM precipitation and temperature bias corrected using non-parametric quantile mapping method

description: The UKCP18 RCM PPE (Met Office Hadley Centre, 2018) projections of precipitation and daily average temperature were bias adjusted using a non-parametric quantile mapping method based on empirical quantiles (Boe et al, 2007, Gudmundsson et al, 2012). The datasets cover the period from December 1980 to November 2080 and are intended for use in climate change impact assessments, where the bias correction helps reduce biases in multiple statistics while maintaining projected climatic changes.

-------------------------------------------------

Met Office Hadley Centre (2018): UKCP18 Regional Projections on a 12km grid over the UK for 1980-2080. CEDA, 8 March 2022. https://catalogue.ceda.ac.uk/uuid/589211abeb844070a95d061c8cc7f604

Boe, J.; Terray, L.; Habets, F. & Martin, E. Statistical and dynamical downscaling of the Seine basin climate for hydro-meteorological studies. International Journal of Climatology, 2007, 27, 1643-1655, doi: 10.1002/joc.1602.

Gudmundsson, L.; Bremnes, J. B.; Haugen, J. E. & Engen-Skaugen, T. Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations - a comparison of methods. Hydrology and Earth System Sciences, 2012, 16, 3383-3390, doi:10.5194/hess-16-3383-2012.

Paper Citation:We kindly ask users of this dataset to cite the paper that describes the dataset. The paper is published and can be accessed via the following link: https://doi.org/10.5194/essd-17-2113-2025.

Please reference the paper as:Reyniers, N., Zha, Q., Addor, N., Osborn, T. J., Forstenhäusler, N., and He, Y.: Two sets of bias-corrected regional UK Climate Projections 2018 (UKCP18) of temperature, precipitation and potential evapotranspiration for Great Britain, Earth Syst. Sci. Data, 17, 2113–2133, https://doi.org/10.5194/essd-17-2113-2025, 2025.
DOI: 10.5281/zenodo.8223024
title: UKCP18 RCM precipitation and temperature bias corrected using non-parametric quantile mapping method

description: The UKCP18 RCM PPE (Met Office Hadley Centre, 2018) projections of precipitation and daily average temperature were bias adjusted using a non-parametric quantile mapping method based on empirical quantiles (Boe et al, 2007, Gudmundsson et al, 2012). The datasets cover the period from December 1980 to November 2080 and are intended for use in climate change impact assessments, where the bias correction helps reduce biases in multiple statistics while maintaining projected climatic changes.

-------------------------------------------------

Met Office Hadley Centre (2018): UKCP18 Regional Projections on a 12km grid over the UK for 1980-2080. CEDA, 8 March 2022. https://catalogue.ceda.ac.uk/uuid/589211abeb844070a95d061c8cc7f604

Boe, J.; Terray, L.; Habets, F. & Martin, E. Statistical and dynamical downscaling of the Seine basin climate for hydro-meteorological studies. International Journal of Climatology, 2007, 27, 1643-1655, doi: 10.1002/joc.1602.

Gudmundsson, L.; Bremnes, J. B.; Haugen, J. E. & Engen-Skaugen, T. Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations - a comparison of methods. Hydrology and Earth System Sciences, 2012, 16, 3383-3390, doi:10.5194/hess-16-3383-2012.

Paper Citation:We kindly ask users of this dataset to cite the paper that describes the dataset. The paper is published and can be accessed via the following link: https://doi.org/10.5194/essd-17-2113-2025.

Please reference the paper as:Reyniers, N., Zha, Q., Addor, N., Osborn, T. J., Forstenhäusler, N., and He, Y.: Two sets of bias-corrected regional UK Climate Projections 2018 (UKCP18) of temperature, precipitation and potential evapotranspiration for Great Britain, Earth Syst. Sci. Data, 17, 2113–2133, https://doi.org/10.5194/essd-17-2113-2025, 2025.