cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.z2m7s4
title: Occurrence Download

description: A dataset containing 32 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Formica puberula",
 "Country is Canada",
 {
 "not" : {
 "InstitutionCode" : [
 "is one of (iNaturalist)"
 ]
 }
 }
 ]
}

 The dataset includes 32 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081918-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.89uv84
title: Occurrence Download

description: A dataset containing 30 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Sardinella sindensis",
 "Year 2000-2026"
 ]
}

 The dataset includes 30 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081919-251120083545085/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.30723869
title: High-accuracy reconstruction of water levels through integrating physics-based and data-driven models: application to the Seine estuary

description: An accurate understanding of river dynamics is vital for preserving ecosystems, supporting economic activities, and tackling climate challenges. This study presents a hybrid modelling approach that combines physics-based models with deep learning to reconstruct high-frequency, long-term water levels in the Seine Estuary, Normandy, France. Deep learning – specifically bidirectional long short-term memory networks – enhances hydrodynamic model outputs by integrating diverse observational and meteorological data. The multi-process framework effectively captures spatial and temporal variability across hydrodynamic zones. Results show the hybrid model outperforms standalone physics-based and deep learning models, reducing RMSE by approximately 58% and improving accuracy by 50–65% during major storm events since 2017. Reconstruction quality is mainly influenced by the relevance of hydrodynamic outputs and observational data, while meteorological inputs play a lesser role due to their coarse resolution. These findings demonstrate the robustness and potential of hybrid models for accurate, reliable reconstructions and projections in complex river systems.
DOI: 10.6084/m9.figshare.30723869.v1
title: High-accuracy reconstruction of water levels through integrating physics-based and data-driven models: application to the Seine estuary

description: An accurate understanding of river dynamics is vital for preserving ecosystems, supporting economic activities, and tackling climate challenges. This study presents a hybrid modelling approach that combines physics-based models with deep learning to reconstruct high-frequency, long-term water levels in the Seine Estuary, Normandy, France. Deep learning – specifically bidirectional long short-term memory networks – enhances hydrodynamic model outputs by integrating diverse observational and meteorological data. The multi-process framework effectively captures spatial and temporal variability across hydrodynamic zones. Results show the hybrid model outperforms standalone physics-based and deep learning models, reducing RMSE by approximately 58% and improving accuracy by 50–65% during major storm events since 2017. Reconstruction quality is mainly influenced by the relevance of hydrodynamic outputs and observational data, while meteorological inputs play a lesser role due to their coarse resolution. These findings demonstrate the robustness and potential of hybrid models for accurate, reliable reconstructions and projections in complex river systems.
DOI: 10.6084/m9.figshare.30490057
title: The 1970 Congo River Basin drought: early warning signals and climate recovery revealed by a Bayesian-based approach

description: Transient processes (TP) in the Congo River Basin (CRB) have received little attention. This study uses the concept of a climate disturbance signal (CDS) to characterize the TP that led to the 1970 drought in the CRB. The CDS was derived from posterior probabilities obtained through a Bayesian approach of gridded precipitation data. Its persistence quantified using Kendall and Moran spatial auto-correlations showed a gradual rise to a peak in 1971, followed by a decline until 1977. This evolution highlights two key insights: the 1970 drought was preceded by more than a decade of early warning signals, and the basin’s climate required nearly 7 years to reach equilibrium. These findings emphasize the crucial role of the TP in hydroclimatology and demonstrate the practical value of the CDS for water management, hydraulic planning, and climate risk mitigation. Furthermore, this study reduces the uncertainty in the spatio-temporal representativeness of the CRB climate.
DOI: 10.6084/m9.figshare.30490057.v2
title: The 1970 Congo River Basin drought: early warning signals and climate recovery revealed by a Bayesian-based approach

description: Transient processes (TP) in the Congo River Basin (CRB) have received little attention. This study uses the concept of a climate disturbance signal (CDS) to characterize the TP that led to the 1970 drought in the CRB. The CDS was derived from posterior probabilities obtained through a Bayesian approach of gridded precipitation data. Its persistence quantified using Kendall and Moran spatial auto-correlations showed a gradual rise to a peak in 1971, followed by a decline until 1977. This evolution highlights two key insights: the 1970 drought was preceded by more than a decade of early warning signals, and the basin’s climate required nearly 7 years to reach equilibrium. These findings emphasize the crucial role of the TP in hydroclimatology and demonstrate the practical value of the CDS for water management, hydraulic planning, and climate risk mitigation. Furthermore, this study reduces the uncertainty in the spatio-temporal representativeness of the CRB climate.
DOI: 10.6084/m9.figshare.30490057.v1
title: The 1970 Congo River Basin drought: early warning signals and climate recovery revealed by a Bayesian-based approach

description: Transient Processes (TP) in the Congo River Basin (CRB) have received little attention.This study uses the concept of a Climate Disturbance Signal (CDS) to characterize the TP that led to the 1970 drought in the CRB. The CDS was derived from posterior probabilities obtained through a Bayesian approach of gridded precipitation data. Its persistence quantified using Kendall and Moran spatial auto-correlations showed a gradual rise to a peak in 1971, followed by a decline until 1977. This evolution highlights two key insights: the 1970 drought was preceded by more than a decade of early warning signals, and the basin’s climate required nearly seven years to reach equilibrium. These findings emphasize the crucial role of the TP in hydroclimatology and demonstrate the practical value of the CDS for water management, hydraulic planning, and climate risk mitigation. Furthermore, This study reduces the uncertainty in the spatiotemporal representativeness of the CRB climate.
DOI: 10.15468/dl.nw7xqm
title: Occurrence Download

description: A dataset containing 105 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((71.881765 22.724046, 71.80562999999999 22.718469, 71.731388 22.701878, 71.660882 22.674684, 71.59586299999999 22.637563, 71.537938 22.591437, 71.48853699999999 22.537449, 71.44887300000001 22.476937, 71.419915 22.411399, 71.402362 22.342453, 71.39663 22.271801, 71.402843 22.201185, 71.42083 22.13234, 71.45013299999999 22.066959, 71.49001800000001 22.006646, 71.539495 21.952878, 71.59734400000001 21.906972, 71.662142 21.87005, 71.732303 21.843013, 71.806111 21.826523, 71.881765 21.820981, 71.957419 21.826523, 72.031227 21.843013, 72.101388 21.87005, 72.166186 21.906972, 72.224035 21.952878, 72.273512 22.006646, 72.31339699999999 22.066959, 72.34269999999999 22.13234, 72.360687 22.201185, 72.3669 22.271801, 72.36116800000001 22.342453, 72.343615 22.411399, 72.314657 22.476937, 72.27499299999999 22.537449, 72.22559200000001 22.591437, 72.16766699999999 22.637563, 72.102648 22.674684, 72.03214199999999 22.701878, 71.9579 22.718469, 71.881765 22.724046))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 105 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081920-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17052836
title: Raw data for the article "A method for oscillation-free dynamic IR compensation during potentiostatic electrolyses"

description: This is a dataset uploaded to Zenodo at doi:10.5281/zenodo.17052837

The dataset contains raw measurement data that served as a basis for the paper titled A Method for Oscillation-Free Dynamic IR Compensation during Potentiostatic Electrolyses", published Small Methods, doi:10.1002/smtd.202501825

All files are either simple, ASCII-coded text files containing tab-separated values (.tsv format) or images (.png format). 

Contents of the dataset are as follows:



eis.tsv: a file containing raw data for the impedance spectra shown in Figure 5 of the paper. This table has a multi-index column, in Python, it can be read with pandas.read_csv("test.tsv", sep="\t", index_col=0, header=[0,1]) .

calcurve.tsv, calspectra.tsv: files containing spectrophotometry calibration curves and spectra based on which the calibration was created. These data were used to create Figure S5 of the Supporting Information.

Files named as "exp05_spectr_070percent_E-1300mV.tsv" or "exp06_echem_100percent_E-1300mV.tsv" correspond to a given experiment (these ones to number 5 or 6, the numbering is consistent with that used in the Supporting Information of the paper). The files contain either measured data recorded during electrolyses (with the tag "echem") or spectroscopic data recorded after electrolysis, for product quantification (with the tag "spect"). The filename also contains the degree of IR compensation used (in the above example, 70 or 100%) and the value of the electrolysis potential (-1300 mV in this example case). Current, resistance and temperature traces are also plotted and saved as .png files using the pattern "exp06_Iplot_070percent_E-1300mV.png", "exp08_Rplot_070percent_E-1325mV.png" or "exp01_Tplot_070percent_E-1275mV.png".
DOI: 10.5281/zenodo.17052837
title: Raw data for the article "A method for oscillation-free dynamic IR compensation during potentiostatic electrolyses"

description: This is a dataset uploaded to Zenodo at doi:10.5281/zenodo.17052837

The dataset contains raw measurement data that served as a basis for the paper titled A Method for Oscillation-Free Dynamic IR Compensation during Potentiostatic Electrolyses", published Small Methods, doi:10.1002/smtd.202501825

All files are either simple, ASCII-coded text files containing tab-separated values (.tsv format) or images (.png format). 

Contents of the dataset are as follows:



eis.tsv: a file containing raw data for the impedance spectra shown in Figure 5 of the paper. This table has a multi-index column, in Python, it can be read with pandas.read_csv("test.tsv", sep="\t", index_col=0, header=[0,1]) .

calcurve.tsv, calspectra.tsv: files containing spectrophotometry calibration curves and spectra based on which the calibration was created. These data were used to create Figure S5 of the Supporting Information.

Files named as "exp05_spectr_070percent_E-1300mV.tsv" or "exp06_echem_100percent_E-1300mV.tsv" correspond to a given experiment (these ones to number 5 or 6, the numbering is consistent with that used in the Supporting Information of the paper). The files contain either measured data recorded during electrolyses (with the tag "echem") or spectroscopic data recorded after electrolysis, for product quantification (with the tag "spect"). The filename also contains the degree of IR compensation used (in the above example, 70 or 100%) and the value of the electrolysis potential (-1300 mV in this example case). Current, resistance and temperature traces are also plotted and saved as .png files using the pattern "exp06_Iplot_070percent_E-1300mV.png", "exp08_Rplot_070percent_E-1325mV.png" or "exp01_Tplot_070percent_E-1275mV.png".
DOI: 10.15468/dl.rj9eg9
title: Occurrence Download

description: A dataset containing 795 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Sardinella zunasi",
 "Year 2000-2026"
 ]
}

 The dataset includes 795 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081921-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3dkcya
title: Occurrence Download

description: A dataset containing 875743 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Human Observation, Specimen, Machine Observation)",
 "Geometry POLYGON((-66.26433 -57.38002,-49.39308 -34.9983,-36.92302 -20.5642,-30.07672 -7.30798,-55.26619 13.81362,-69.51662 22.85404,-84.45183 24.38338,-93.23725 25.73627,-96.31215 25.49861,-99.38705 27.30836,-100.70486 29.54867,-102.19838 30.15824,-103.34049 29.47222,-104.74616 30.31005,-107.38178 31.8151,-109.49028 31.516,-112.82874 31.88972,-115.20081 32.92808,-117.92429 32.03879,-115.55222 26.1313,-110.54453 15.31719,-94.02794 6.90122,-76.45793 -21.72681,-76.69924 -57.18887,-66.26433 -57.38002))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Amphibia",
 "Year 1950-2025"
 ]
}

 The dataset includes 875743 records from 919 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081883-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.adncwa
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002112-171219132708484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mqdjpe
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057811-160910150852091/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.eyvbnv
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002123-171219132708484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sw1pvs
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014975-160311141623029/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.b6echr
title: Occurrence Download

description: A dataset containing 32 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 32 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002049-190813142620410/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.98hpnu
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002116-171219132708484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nxoodw
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000010-160430102343922/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qqz37c
title: Occurrence Download

description: A dataset containing 1928297338 species occurrences available in GBIF matching the query:
{
 "BasisOfRecord" : [
 "is Human Observation"
 ]
}

 The dataset includes 1928297338 records from 24069 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020771-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1200289.10fu
title: SH1200289.10FU

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/sh1200291.10fu
title: SH1200291.10FU

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/sh1200293.10fu
title: SH1200293.10FU

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/sh1200294.10fu
title: SH1200294.10FU

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.