cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh1087425.10fu
title: SH1087425.10FU

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/sh1087427.10fu
title: SH1087427.10FU

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/sh1087428.10fu
title: SH1087428.10FU

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/sh1087430.10fu
title: SH1087430.10FU

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/sh1087432.10fu
title: SH1087432.10FU

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/sh1087434.10fu
title: SH1087434.10FU

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/sh1087435.10fu
title: SH1087435.10FU

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/sh1087437.10fu
title: SH1087437.10FU

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.vu2wyx
title: Occurrence Download

description: A dataset containing 10 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Portugal",
 "TaxonKey is Armeria gaditana Boiss.",
 "Year 1973-2025"
 ]
}

 The dataset includes 10 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005369-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14902292
title: Processed per-frame and swim bout data for Constant Temperature dataset

description: ConstantTemperature_ProcessedDataframes_Pickle.zip

For each fish in the gradient experiments there is one file named according to <ExperimentName>.nwb&&<lane>_fish.pkl and one file named according to <ExperimentName>.nwb&&<lane>_bout.pkl. Each of these files is a pickled pandas dataframes (to be loaded via pandas.read_pickle ). The *_fish files contain frame-by-frame information about position, speed and heading while the *_bout files contain per-swim-bout start/end information as well as kinematics and states.

Fish dataframe structure




Temperature
X Position
Y Position
Heading
Raw X
Raw Y
Instant speed


The temperature at the fish position at the given frame
The filtered X position of the fish at the given frame in mm
The filtered Y position of the fish at the given frame in mm
The heading of the fish at the given frame in radians
The raw tracked X Position
The raw tracked Y Position
The instantaneous speed of the fish at the given frame in mm/s




Bout dataframe structure




State
Gradient direction
Original index
Start
Stop
Peak speed
Displacement
Angle change
IBI


The annotated swim mode
The cosine of the angle of the bout vector with respect to the y direction in the chambe (equivalent to gradient direction in gradient experiments)
The original index of the bout (since bouts close to the arena edge were filtered out)
The start camera frame of the bout
The end camera frame of the bout
The maximal speed in mm/s reached during the bout
The displacement of the bout in mm
The angle change of the bout in radians
The waiting time since the previous bout (interbout interval) in ms


Temperature
Prev Delta T
1s Delta T
Delta X
Delta Y
Prev angle change
X Position
Y Position
Heading


The temperature at the start of the bout
The temperature change across the previous bout
The temperature change across the preceding second
The movement in the X direction in mm
The movement in the Y direction in mm
The angle change of the previous bout
The x position at the start of the bout
The y position at the start of the bout
The fish heading at the start of the bout
DOI: 10.5281/zenodo.14902293
title: Processed per-frame and swim bout data for Constant Temperature dataset

description: ConstantTemperature_ProcessedDataframes_Pickle.zip

For each fish in the gradient experiments there is one file named according to <ExperimentName>.nwb&&<lane>_fish.pkl and one file named according to <ExperimentName>.nwb&&<lane>_bout.pkl. Each of these files is a pickled pandas dataframes (to be loaded via pandas.read_pickle ). The *_fish files contain frame-by-frame information about position, speed and heading while the *_bout files contain per-swim-bout start/end information as well as kinematics and states.

Fish dataframe structure




Temperature
X Position
Y Position
Heading
Raw X
Raw Y
Instant speed


The temperature at the fish position at the given frame
The filtered X position of the fish at the given frame in mm
The filtered Y position of the fish at the given frame in mm
The heading of the fish at the given frame in radians
The raw tracked X Position
The raw tracked Y Position
The instantaneous speed of the fish at the given frame in mm/s




Bout dataframe structure




State
Gradient direction
Original index
Start
Stop
Peak speed
Displacement
Angle change
IBI


The annotated swim mode
The cosine of the angle of the bout vector with respect to the y direction in the chambe (equivalent to gradient direction in gradient experiments)
The original index of the bout (since bouts close to the arena edge were filtered out)
The start camera frame of the bout
The end camera frame of the bout
The maximal speed in mm/s reached during the bout
The displacement of the bout in mm
The angle change of the bout in radians
The waiting time since the previous bout (interbout interval) in ms


Temperature
Prev Delta T
1s Delta T
Delta X
Delta Y
Prev angle change
X Position
Y Position
Heading


The temperature at the start of the bout
The temperature change across the previous bout
The temperature change across the preceding second
The movement in the X direction in mm
The movement in the Y direction in mm
The angle change of the previous bout
The x position at the start of the bout
The y position at the start of the bout
The fish heading at the start of the bout
DOI: 10.5281/zenodo.14902972
title: Navigation Model Parameters

description:     sim_model_store.hdf5

This HDF5 file contains all stan samples for all model fits (transition and emission). Note, all parameters are in the input space (in spite of being fit after QR decomposition), however, temperatures were centered by subtracting 25 before passing to the model. The file has the following structure:




Displacement_Gamma [group]
 
 


 
Mode_-1 [group for reversal mode]
 


 
 
bg_alpha: For gamma distribution fit on all displacements within mode the alpha-parameter


 
 
bg_beta: For gamm distribution fit on all displacements within mode the beta-paramter


 
 
glm_mu_icept: stan samples of intercept of glm calculating the average of the input dependent gamma distribution


 
 
glm_mu_orig: stan samples of glm parameters of glm calculating the average of the input dependent gamma distribution


 
Mode_0 [group for general mode]
Same type of content as Mode_-1


 
Mode_1 [group for persistent mode]
Same type of content as Mode_-1


IBI_Gamma [group]
Same subgroups and type of content as Displacement_Gamma
 


Turn_GLM_GMM [group]
 
 


 
Mode_-1 [group for reversal mode]
 


 
 
turn_alpha: For the turn related gamma distributions the alpha parameter


 
 
turn_beta: For the turn related gamma distributions the beta parameter


 
 
straight_std: For the straight-swim gaussian, the standard deviation


 
 
turn_avg_mix: For all swims across the mode, the average mixing coefficients


 
 
gmm_mix_icept: stan samples of glm parameters to compute input dependent mixing coefficients


 
 
gmm_mix_orig: stan samples of glm parameters to compute input dependent mixing coefficients


 
Mode_0 [group for general mode]
Same type of content as Mode_-1


 
Mode_1 [group for persistent mode]
Same type of content as Mode_-1


Swim_mode_transit_Activity [group]
 
 


 
 
mm_icept: stan samples of intercept of glms that control activity dependent state transitions


 
 
mm_orig: stan samples of glm parameters for glms that control activity dependent state transitions


Swim_mode_transit_Order_0 [group]
 
 


 
 
mm_icept: stan samples of intercept for fixed state transitions


Swim_mode_transit_Order_1 [group]
 
 


 
 
mm_icept: stan samples of intercept of glms that control stimulus dependent state transitions


 
 
mm_orig: stan samples of glm parameters for glms that control stimulus dependent state transitions


Swim_mode_transit_Order_2 [group]
 
Same type of content as Swim_mode_transit_Order_1


Swim_mode_transit_Order_3 [group]
 
Same type of content as Swim_mode_transit_Order_1
DOI: 10.5281/zenodo.15007957
title: MINE weights for medullar response type cluster fits

description: For each medullar response type, contains the trained weights as saved by MINE. See https://doi.org/10.7554/eLife.83289 for a description of MINE.
DOI: 10.5281/zenodo.15007958
title: MINE weights for medullar response type cluster fits

description: For each medullar response type, contains the trained weights as saved by MINE. See https://doi.org/10.7554/eLife.83289 for a description of MINE.
DOI: 10.15468/dl.usjd4s
title: Occurrence Download

description: A dataset containing 17170 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Otiorhynchus sulcatus (Fabricius, 1775)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true"
 ]
}

 The dataset includes 17170 records from 268 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005372-250310093411724/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.28583363.v1
title: GROVES regression data

description: Data from regression analyses performed on GROVES data.
DOI: 10.6084/m9.figshare.28583363
title: GROVES regression data

description: Data from regression analyses performed on GROVES data.
DOI: 10.15468/dl.tkeewt
title: Occurrence Download

description: A dataset containing 796 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Acanthophis praelongus Ramsay, 1877"
 ]
}

 The dataset includes 796 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005373-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14507588
title: Data related to the paper "Improving the equatorial components of effective angular momentum forecasts based on real environments"
DOI: 10.5281/zenodo.14507589
title: Data related to the paper "Improving the equatorial components of effective angular momentum forecasts based on real environments"
DOI: 10.5281/zenodo.15007545
title: Data related to the paper "Improving the equatorial components of effective angular momentum forecasts based on real environments"
DOI: 10.5281/zenodo.15014311
title: Data related to the paper "Improving the equatorial components of effective angular momentum forecasts based on real environments"
DOI: 10.15468/dl.rmvnrk
title: Occurrence Download

description: A dataset containing 13433 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Yucca schidigera Roezl ex Ortgies",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Human Observation, Living Specimen, Occurrence evidence)"
 ]
}

 The dataset includes 13433 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005374-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15014304
title: ostorhinchus_chrysopomus_zanfona_1.fasta.gz
DOI: 10.5281/zenodo.15014305
title: ostorhinchus_chrysopomus_zanfona_1.fasta.gz
DOI: 10.15468/dl.4g2vda
title: Occurrence Download

description: A dataset containing 16 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Plectranthus esculentus N.E.Br.",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Human Observation, Living Specimen, Occurrence evidence)"
 ]
}

 The dataset includes 16 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005376-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9xjfj2
title: Occurrence Download

description: A dataset containing 20484 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tenodera sinensis Saussure, 1871",
 "HasGeospatialIssue is false",
 "HasCoordinate is true"
 ]
}

 The dataset includes 20484 records from 54 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005375-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.e3n2dd
title: Occurrence Download

description: A dataset containing 59894 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-154.96819 40.70952,-57.69936 40.70952,-57.69936 79.60039,-154.96819 79.60039,-154.96819 40.70952))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Phyciodes tharos Drury, 1773",
 "Year 1951-2025"
 ]
}

 The dataset includes 59894 records from 85 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005360-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2yvxa8
title: Occurrence Download

description: A dataset containing 361 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "OccurrenceStatus is Present",
 "StateProvince is Zacatecas",
 "TaxonKey is Melanerpes aurifrons (Wagler, 1829)"
 ]
}

 The dataset includes 361 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005378-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rfkg7s
title: Occurrence Download

description: A dataset containing 6353 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Acanthospermum hispidum DC."
 ]
}

 The dataset includes 6353 records from 359 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005381-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wajrry
title: Occurrence Download

description: A dataset containing 581 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Scolioneura betuleti (Klug, 1816)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true"
 ]
}

 The dataset includes 581 records from 56 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005383-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.