cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh0112242.10fu
title: SH0112242.10FU

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: 10.13145/bacdive120607.20241212.9.2
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive120607.20251217.10
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.15156/bio/sh0112243.10fu
title: SH0112243.10FU

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: 10.13145/bacdive120608.20241212.9.2
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive120608.20251217.10
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.15156/bio/sh0112244.10fu
title: SH0112244.10FU

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: 10.13145/bacdive120609.20241212.9.2
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.15156/bio/sh0112245.10fu
title: SH0112245.10FU

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: 10.13145/bacdive120609.20251217.10
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive120610.20241212.9.2
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.15156/bio/sh0112246.10fu
title: SH0112246.10FU

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: 10.15468/dl.6j2h6f
title: Occurrence Download

description: A dataset containing 1785 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Swiss National Databank of Vascular Plants",
 "TaxonKey is Typha minima Funck ex Hoppe"
 ]
}

 The dataset includes 1785 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025227-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48597/s389-jqaj
title: Measurement of Ozone at Neumayer

description: Measurement of Ozone at Neumayer
DOI: 10.48597/22be-c7fq
title: Measurement of Ozone at Neumayer

description: Measurement of Ozone at Neumayer
DOI: 10.48597/6k2a-yct9
title: Measurement of Ozone at Neumayer

description: Measurement of Ozone at Neumayer
DOI: 10.48597/7nbs-sje8
title: Measurement of Ozone at Neumayer

description: Measurement of Ozone at Neumayer
DOI: 10.48597/8wru-brhv
title: Measurement of Ozone at Neumayer

description: Measurement of Ozone at Neumayer
DOI: 10.48597/ad9n-wn7a
title: Measurement of Ozone at Neumayer

description: Measurement of Ozone at Neumayer
DOI: 10.48597/c9ms-eqfp
title: Measurement of Ozone at Neumayer

description: Measurement of Ozone at Neumayer
DOI: 10.48597/nssg-7yqm
title: Measurement of Ozone at Neumayer

description: Measurement of Ozone at Neumayer
DOI: 10.48597/pnf2-34dh
title: Measurement of Ozone at Neumayer

description: Measurement of Ozone at Neumayer
DOI: 10.48597/qu8v-42gc
title: Ozone at Neumayer

description: Measurement of Ozone at Neumayer
DOI: 10.48597/r83g-b379
title: Measurement of Ozone at Neumayer

description: Measurement of Ozone at Neumayer
DOI: 10.48597/v75u-7k5s
title: Measurement of Ozone at Neumayer

description: Measurement of Ozone at Neumayer
DOI: 10.48597/3xsr-t35m
title: Measurement of Ozone at Neumayer

description: Measurement of Ozone at Neumayer
DOI: 10.48597/speu-x6md
title: Measurement of Ozone at Neumayer

description: Measurement of Ozone at Neumayer
DOI: 10.48597/uhs3-qveq
title: Measurement of Ozone at Neumayer

description: Measurement of Ozone at Neumayer
DOI: 10.48597/xax5-7p98
title: Measurement of Ozone at Neumayer

description: Measurement of Ozone at Neumayer
DOI: 10.15468/dl.qex4dy
title: Occurrence Download

description: A dataset containing 169 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Italy",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Festuca riccerii Foggi & G.Rossi",
 "Year 1960-2025"
 ]
}

 The dataset includes 169 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025228-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fcca8a
title: Occurrence Download

description: A dataset containing 204 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Saudi Arabia",
 "OccurrenceStatus is Present",
 "TaxonKey is Vachellia tortilis (Forssk.) Galasso & Banfi"
 ]
}

 The dataset includes 204 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025229-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5066/p13dobtm
title: USGS CoastCam at Isla Verde, Puerto Rico: 2022-2023 Calibration Data

description: Two digital video cameras were installed at Isla Verde, Puerto Rico (PR); camera 1 faced northeast offshore and camera 2 faced east-northeast along the beach. Every hour during daylight hours, daily starting in August 2022, the cameras collected raw video and produced snapshots and time-averaged image products. This data release includes the necessary intrinsic orientation (IO) and extrinsic orientation (EO) calibration data to utilize imagery to make quantitative measurements. The cameras are part of a U.S. Geological Survey (USGS) research project to study the beach and nearshore environment. USGS researchers analyzed the imagery collected from this camera to remotely sense a range of information including shoreline position, sandbar migration, wave run-up on the beach, alongshore currents, and nearshore bathymetry.
DOI: 10.5281/zenodo.15074964
title: Deep learning in the abyss: a stratified Physics Informed Neural Network for data assimilation

description: READ ME:

Data required to run the codes attached to the paper "Deep learning in the abyss: a stratified Physics Informed Neural Network for data assimilation" available on GitHub (https://github.com/VadimLimousin/StrAss-PINN/). The data requirements are: 



To run the introductory notebook: params.in, vars.nc, coords_128_100j, mask_128_100j, psi1_128_100j, std_128_100j

To run the full method: params.in, vars.nc, coords, mask, psi1, std
description: The reconstruction of deep ocean currents is a major challenge in data assimilation due to the scarcity of interior data. In this work, we present a proof of concept for deep ocean flow reconstruction using a Physics-Informed Neural Network (PINN), a machine learning approach that offers an alternative to traditional data assimilation methods. We introduce an efficient algorithm called StrAssPINN (for Stratified PINNs), which assigns a separate network to each layer of the ocean model while allowing them to interact during training. The neural network takes spatiotemporal coordinates as input and predicts the velocity field at those points. Using a SIREN architecture (a multilayer perceptron with sine activation functions), which has proven effective in various contexts, the network is trained using both available observational data and dynamical priors enforced at several colocation points. We apply this method to pseudo-observed ocean data generated from a 3-layer quasi-geostrophic model, where the pseudo-observations include surface-level data akin to SWOT observations of sea surface height, interior data similar to ARGO floats, and a limited number of deep ARGO-like measurements in the lower layers. Our approach successfully reconstructs ocean flows in both the interior and surface layers, demonstrating a strong ability to resolve key ocean mesoscale features, including vortex rings, eastward jets associated with potential vorticity fronts, and smoother Rossby waves. This work serves as a prelude to applying StrAssPINN to real-world observational data.
DOI: 10.5281/zenodo.15074965
title: Deep learning in the abyss: a stratified Physics Informed Neural Network for data assimilation

description: READ ME:

Data required to run the codes attached to the paper "Deep learning in the abyss: a stratified Physics Informed Neural Network for data assimilation" available on GitHub (https://github.com/VadimLimousin/StrAss-PINN/). The data requirements are: 



To run the introductory notebook: params.in, vars.nc, coords_128_100j, mask_128_100j, psi1_128_100j, std_128_100j

To run the full method: params.in, vars.nc, coords, mask, psi1, std
description: The reconstruction of deep ocean currents is a major challenge in data assimilation due to the scarcity of interior data. In this work, we present a proof of concept for deep ocean flow reconstruction using a Physics-Informed Neural Network (PINN), a machine learning approach that offers an alternative to traditional data assimilation methods. We introduce an efficient algorithm called StrAssPINN (for Stratified PINNs), which assigns a separate network to each layer of the ocean model while allowing them to interact during training. The neural network takes spatiotemporal coordinates as input and predicts the velocity field at those points. Using a SIREN architecture (a multilayer perceptron with sine activation functions), which has proven effective in various contexts, the network is trained using both available observational data and dynamical priors enforced at several colocation points. We apply this method to pseudo-observed ocean data generated from a 3-layer quasi-geostrophic model, where the pseudo-observations include surface-level data akin to SWOT observations of sea surface height, interior data similar to ARGO floats, and a limited number of deep ARGO-like measurements in the lower layers. Our approach successfully reconstructs ocean flows in both the interior and surface layers, demonstrating a strong ability to resolve key ocean mesoscale features, including vortex rings, eastward jets associated with potential vorticity fronts, and smoother Rossby waves. This work serves as a prelude to applying StrAssPINN to real-world observational data.
DOI: 10.5281/zenodo.6559355
title: Selected data(s) from : Five-dimensional optical data storage based on ellipse orientation and fluorescence intensity in a silver-sensitized commercial glass

description: The data selected is based on the figures below, published in the linked article (see the doi).


- Figure 1. (a) Femtosecond laser tight focusing in the silver-containing glass, leading to the production of fluorescent silver clusters at its periphery. (b) SLM holographic phase masks with an additional cylindrical profile leading to an elliptical pattern by DLW. (c) Oriented elliptical patterns obtained by SLM phase mask manipulation, corresponding to 24 = 16 orientation-encoded levels. (Only picture)


- Figure 2. Fabricated fluorescence calibration matrix. (a) Confocal image of all basic storage units composed by 16 intensity levels and 16 orientation levels. (b) Measured fluorescence intensity versus incident DLW intensity for the 5D decoding process. (Pictures, opj file, csv datas)


-  Figure 3. (a,b) are the encoded images of two Nobel laureates in 16 orientation levels and 16 intensity levels, respectively. (c) 100 × 100 entangled patterns among 16 × 16 intensity and orientation levels. (d) The fluorescence calibration matrix was fabricated for decoding (fluorescence excitation at 405 nm). (Pictures, cvs datas)



	
ANR_ ARCHIFLUO_ICMCB_Article_Micromachines_Figure3_2020-10-21_V01 : Figure 3
	
ANR_ ARCHIFLUO_ICMCB_Article_Micromachines_Figure3a_2020-10-21_V01 : Original image oritentation
	
ANR_ ARCHIFLUO_ICMCB_Article_Micromachines_Figure3b_2020-10-21_V01 : Original image intensity
	
ANR_ ARCHIFLUO_ICMCB_Article_Micromachines_Figure3c_Figure3d_2020-10-21_V01 : DLW image
	
ANR_ ARCHIFLUO_ICMCB_Article_Micromachines_Figure3_datas_IICT4BF_2020-10-21_V01 : Intensity image converted to 4 bit format
	
ANR_ ARCHIFLUO_ICMCB_Article_Micromachines_Figure3_datas_OICT4BF_T2020-10-21_V01 : Orientation image converted to 4 bit format
	
ANR_ ARCHIFLUO_ICMCB_Article_Micromachines_Figure3_datas_OICT4BFL_2020-10-21_V01 : Orientation image converted to 4 bit format level



- Figure 4. (a,b) Retrieved images from the initial images of Figure 3a,b, respectively. (c,d) Histograms of the level difference between original and decoded levels for the orientation direction and the fluorescence intensity, respectively. (Picture and csv datas)


- Figure 5. (a) Confocal top-view image of one single elliptically-shaped storage unit fabricated by using type A DLW. (b) Fluorescence intensity profile along the horizontal and vertical cross section at focal plane. (c) Fluorescence intensity profile and Gaussian fitting along the z-axis (depth). (Picture, opj file, csv datas)
DOI: 10.5281/zenodo.6559354
title: Selected data(s) from : Five-dimensional optical data storage based on ellipse orientation and fluorescence intensity in a silver-sensitized commercial glass

description: The data selected is based on the figures below, published in the linked article (see the doi).


- Figure 1. (a) Femtosecond laser tight focusing in the silver-containing glass, leading to the production of fluorescent silver clusters at its periphery. (b) SLM holographic phase masks with an additional cylindrical profile leading to an elliptical pattern by DLW. (c) Oriented elliptical patterns obtained by SLM phase mask manipulation, corresponding to 24 = 16 orientation-encoded levels. (Only picture)


- Figure 2. Fabricated fluorescence calibration matrix. (a) Confocal image of all basic storage units composed by 16 intensity levels and 16 orientation levels. (b) Measured fluorescence intensity versus incident DLW intensity for the 5D decoding process. (Pictures, opj file, csv datas)


-  Figure 3. (a,b) are the encoded images of two Nobel laureates in 16 orientation levels and 16 intensity levels, respectively. (c) 100 × 100 entangled patterns among 16 × 16 intensity and orientation levels. (d) The fluorescence calibration matrix was fabricated for decoding (fluorescence excitation at 405 nm). (Pictures, cvs datas)



	
ANR_ ARCHIFLUO_ICMCB_Article_Micromachines_Figure3_2020-10-21_V01 : Figure 3
	
ANR_ ARCHIFLUO_ICMCB_Article_Micromachines_Figure3a_2020-10-21_V01 : Original image oritentation
	
ANR_ ARCHIFLUO_ICMCB_Article_Micromachines_Figure3b_2020-10-21_V01 : Original image intensity
	
ANR_ ARCHIFLUO_ICMCB_Article_Micromachines_Figure3c_Figure3d_2020-10-21_V01 : DLW image
	
ANR_ ARCHIFLUO_ICMCB_Article_Micromachines_Figure3_datas_IICT4BF_2020-10-21_V01 : Intensity image converted to 4 bit format
	
ANR_ ARCHIFLUO_ICMCB_Article_Micromachines_Figure3_datas_OICT4BF_T2020-10-21_V01 : Orientation image converted to 4 bit format
	
ANR_ ARCHIFLUO_ICMCB_Article_Micromachines_Figure3_datas_OICT4BFL_2020-10-21_V01 : Orientation image converted to 4 bit format level



- Figure 4. (a,b) Retrieved images from the initial images of Figure 3a,b, respectively. (c,d) Histograms of the level difference between original and decoded levels for the orientation direction and the fluorescence intensity, respectively. (Picture and csv datas)


- Figure 5. (a) Confocal top-view image of one single elliptically-shaped storage unit fabricated by using type A DLW. (b) Fluorescence intensity profile along the horizontal and vertical cross section at focal plane. (c) Fluorescence intensity profile and Gaussian fitting along the z-axis (depth). (Picture, opj file, csv datas)
DOI: 10.5281/zenodo.14644143
title: Dataset and machine learning models for post-earthquake residual vertical load-carrying capacity (VLCC) assessment of RC bridge bents

description: This upload includes the collected dataset and trained machine learning models (based on Matlab platform) for post-earthquake residual vertical load-carrying capacity (VLCC) assessment of reinforced concrete (RC) single-column and double-column bridge bents. The input variables (features) are structural parameters of bridge bents. The output variables (labels) are post-earthquake residual vertical load-carrying capacity (VLCC) of laterally damaged RC bridge bents corresponding to an inelastic index (II) of 0.5 and 1.0. The dataset contains 860 post-earthquake residual VLCC results gathered by conducting cyclic pushover analyses and pushdown analyses for randomly sampled bridge bents. Machine learning models are developed using three popular machine learning algorithms, i.e., support vector regression (SVR), artificial neural network (ANN), and regression tree ensembles (RTE).
DOI: 10.5281/zenodo.14644144
title: Dataset and machine learning models for post-earthquake residual vertical load-carrying capacity (VLCC) assessment of RC bridge bents

description: This upload includes the collected dataset and trained machine learning models (based on Matlab platform) for post-earthquake residual vertical load-carrying capacity (VLCC) assessment of reinforced concrete (RC) single-column and double-column bridge bents. The input variables (features) are structural parameters of bridge bents. The output variables (labels) are post-earthquake residual vertical load-carrying capacity (VLCC) of laterally damaged RC bridge bents corresponding to an inelastic index (II) of 0.5 and 1.0. The dataset contains 860 post-earthquake residual VLCC results gathered by conducting cyclic pushover analyses and pushdown analyses for randomly sampled bridge bents. Machine learning models are developed using three popular machine learning algorithms, i.e., support vector regression (SVR), artificial neural network (ANN), and regression tree ensembles (RTE).
DOI: 10.15468/dl.gxv5mq
title: Occurrence Download

description: A dataset containing 23921671 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "IucnRedListCategory is VU",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 23921671 records from 14063 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025201-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.