cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.t74d7c
title: Occurrence Download

description: A dataset listing the 35 species recorded in GBIF matching the query:
{
 "and" : [
 "DatasetKey is one of (Botanical Database of Southern Africa (BODATSA): Botanical Collections, iNaturalist Research-grade Observations, Natural History Museum (London) Collection Specimens)",
 "Geometry POLYGON((25.94539 -27.79081,27.62772 -27.79081,27.62772 -26.70931,25.94539 -26.70931,25.94539 -27.79081))",
 "IucnRedListCategory is one of (CR, EN, VU, NT, DD)"
 ]
}

 The dataset's 35 records were derived from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007053-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9s9rye
title: Occurrence Download

description: A dataset containing 1459 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((141.78325 45.86785,142.39822 45.86785,142.39822 46.5,141.78325 46.5,141.78325 45.86785))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Tracheophyta"
 ]
}

 The dataset includes 1459 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007006-250227182400228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5285/8e910ac8-6f52-4548-bbcb-78474b559294
title: Annual population counts of multiple species of seal, Signy Island, 1974-2024

description: The seal population on Signy Island has been monitored since 1974. The total number of adult seals were counted in February each year. The island was divided into 6 zones, and the number of Antarctic Fur seals (Arctocephalus gazella), Elephant seals (Mirounga leonina), Weddell seals (Leptonychotes weddellii) and other seal species in each zone were counted, typically over several days. For Fur seals the number of pups, white seals and entangled seals were also recorded. The first recorded count was conducted in 1974 and counts have been done annually since 1985.

This work was funded by Natural Environment Research Council (UK) core funding to the British Antarctic Survey.
description: The coastal area of Signy Island was divided into six standard zones which were each divided into a number of regions. The zone boundaries remained unaltered between survey years. Within each zone, all seals present were counted. 

Direct line-of-sight ground counts, using binoculars, took place led by experienced observers using a consistent methodology. Pairs of observers used tally counters to record each individual adult seal sighted on land and on sea ice adjacent to the coast of each zone, the whole census being completed over a single day prior to 1995 and over two or three days (in February) from 1997 (no data collected in 1996 and 2021) to 2024. In some years, where possible, fur seals, pups and sub-adults were also counted in some years. 
The data presented here comprises the individual area counts for each survey year.
description: Binoculars
Hand-held tally counter
description: Annual surveys followed consistent methodology led by experienced biologists. All personnel were familiar with identifying the three species of seal and care was always taken to examine all areas on the surveys, in order to ensure no seals were missed. Surveys took place in dry and calm weather conditions as high levels of precipitation and strong winds are known to drive large numbers of Antarctic fur seals into the sea for the duration of a storm at Signy Island. Years with no data indicate personnel had not arrived on Signy in time to carry out this activity. For additional information see Waluda et al 2010
DOI: 10.15468/dl.nhtqgp
title: Occurrence Download

description: A dataset containing 4356 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Bhutan, India)",
 "OccurrenceStatus is Present",
 "TaxonKey is Rhododendron L."
 ]
}

 The dataset includes 4356 records from 61 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007060-250227182430271/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.26245895.v6
title: Dataset for Ambiguous Resonances in Multipulse Quantum Sensing with NVs

description: This dataset contains simulations for ambiguous resonances present in XY8-M dynamical decoupling multipulse sequences with nitrogen vacancy (NV) color centers in diamonds. Over 10<sup>4</sup> combinations of experimental parameters as the order M, magnetic field B<sub>0</sub>, misalignment angle θ and <sup>13</sup>C families are considered for ambiguous resonances arising from <sup>15</sup>N and <sup>13</sup>C nuclear spins coupled to NVs. For a detailed discussion about the physics of these ambiguous resonances please refer to the arXiv paper at the related materials.For efficient storage and quick retrieval, the dataset is provided as an HDF5 file (dataset.h5), which can be visualized via the graphical user interface (GUI) provided here (dataset_gui.py). Both of the files should be downloaded and stored in the same folder. The GUI is based on the Python bindings for Qt6, with dependencies on NumPy, Matplotlib, PyQt6/PySide6, HDF5 and PyTables packages. Thus, we recommend creating a conda virtual environment with:<b>conda create --name dataset-env python </b><b>numpy matplotlib pyside6 pytables</b><b>conda activate dataset-env</b>To launch the GUI, one can simply run the following command in the cloned repository within the conda environment:<b>python dataset_gui.py</b>This will launch the GUI with the standard HDF5 file. However, if the user wants to have a different dataset file or different path, the --dataset flag can be used as follows:<b>python dataset_gui.py --dataset /PATH/CUSTOM_DATASET.hdf5</b>The use of the GUI is intuitive and self-explanatory. As shown in the screenshot, there are two tabs, one for <sup>15</sup>N and the other for <sup>13</sup>C ambiguous resonances. The m<sub>s</sub> state of the electronic spin transition needs to be chosen, with the order M of the XY8 sequence, the bias magnetic field B<sub>0</sub>, minimum and maximum tau or f range. In addition, for <sup>15</sup>N, the misalignment angle θ must be specified and for <sup>13</sup>C, the carbon family. The expected resonances from the gyromagnetic ratios of other nuclear spins can also be compared with. A text file with experimental data can be plotted together with the simulation for spectral disambiguation, with the conditions that it can be read by numpy.loadtxt and its first column representing τ or f in the selected units and the second indicating the XY8 intensity. Finally, the plot can be customized and exported. New simulations for ambiguous resonances will be continuously added to the dataset. Continued open-source development of the GUI occurs at https://github.com/QISS-HZB/ambiguous-resonances-dataset.<br>If for some reason the files cannot be downloaded from Figshare, please use the backup link: https://box.fu-berlin.de/s/ajm6wijbKWJQKQS .(optional) To make sure that the dataset was downloaded correctly, a sha256 checksum can be performed with<b>sha256sum dataset.h5</b>It should match 35a56d84b267179a3becf41195f40d8a067c81613c3ef320cb4ecbd64a198428 .<br><br>Dataset: version 1.6GUI: version 1.6
DOI: 10.15468/dl.t5f36c
title: Occurrence Download

description: A dataset containing 3424 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Colombia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Inga edulis Mart."
 ]
}

 The dataset includes 3424 records from 131 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007008-250227182400228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.35097/gufvnqfty9wakqe3
title: MobilKULT Welle 1
DOI: 10.15468/dl.8kyphj
title: Occurrence Download

description: A dataset containing 2243 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is South America",
 "Country is one of (Brazil, Argentina, Paraguay)",
 "TaxonKey is Asio stygius (Wagler, 1832)",
 "Year 1000-2024"
 ]
}

 The dataset includes 2243 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007009-250227182400228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.drsm5a
title: Occurrence Download

description: A dataset containing 3921 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is South America",
 "Country is one of (Argentina, Brazil, Paraguay)",
 "TaxonKey is Asio flammeus (Pontoppidan, 1763)",
 "Year 1000-2024"
 ]
}

 The dataset includes 3921 records from 34 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007010-250227182400228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.epzx3w
title: Occurrence Download

description: A dataset containing 15 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-9.1787 38.70655,-9.18279 38.71047,-9.1841 38.71733,-9.19375 38.719,-9.1941 38.71624,-9.19313 38.71622,-9.19267 38.71187,-9.18994 38.71027,-9.18449 38.70583,-9.1787 38.70655))",
 "TaxonKey is Amphibia"
 ]
}

 The dataset includes 15 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007061-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jhp9fp
title: Occurrence Download

description: A dataset containing 4592 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is South America",
 "Country is one of (Brazil, Paraguay, Argentina)",
 "TaxonKey is Nyctibius grandis (J.F.Gmelin, 1789)",
 "Year 1000-2024"
 ]
}

 The dataset includes 4592 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007062-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.86a3db
title: Occurrence Download

description: A dataset containing 20222 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Dictamnus albus L."
 ]
}

 The dataset includes 20222 records from 334 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007063-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5683/sp2/soivbw
title: Data from: Ancestral reconstruction of sunflower karyotypes reveals non-random chromosomal evolution

description: &lt;b&gt;Abstract&lt;/b&gt;&lt;br/&gt;&lt;p&gt;Mapping the chromosomal rearrangements between species can inform our understanding of genome evolution, reproductive isolation, and speciation. Here we present a novel algorithm for identifying regions of synteny in pairs of genetic maps, which is implemented in the accompanying R package, syntR. The syntR algorithm performs as well as previous methods while being systematic and repeatable and can be used to map chromosomal rearrangements in any group of species. In addition, we present a systematic survey of chromosomal rearrangements in the annual sunflowers, which is a group known for extreme karyotypic diversity. We build high-density genetic maps for two subspecies of the prairie sunflower&lt;em&gt;,&lt;/em&gt; &lt;em&gt;Helianthus&lt;/em&gt; &lt;em&gt;petiolaris&lt;/em&gt; ssp. &lt;em&gt;petiolaris&lt;/em&gt; and &lt;em&gt;H. petiolaris&lt;/em&gt; ssp. &lt;em&gt;fallax.&lt;/em&gt; Using &lt;em&gt;syntR&lt;/em&gt;, and we identify blocks of synteny between these two subspecies and previously published high-density genetic maps. We reconstruct ancestral karyotypes for annual sunflowers using those synteny blocks and conservatively estimate that there have been 7.9 chromosomal rearrangements per million years – a high rate of chromosomal evolution. Although the rate of inversion is even higher than the rate of translocation in this group, we further find that every extant karyotype is distinguished by between 1 and 3 translocations involving only 8 of the 17 chromosomes. This non-random exchange suggests that specific chromosomes are prone to translocation and may thus contribute disproportionately to widespread hybrid sterility in sunflowers. These data deepen our understanding of chromosome evolution and confirm that &lt;em&gt;Helianthus&lt;/em&gt; has an exceptional rate of chromosomal rearrangement that may facilitate similarly rapid diversification.&lt;/p&gt;
description: &lt;b&gt;Usage notes&lt;/b&gt;&lt;br /&gt;&lt;p&gt;syntR is an R package for the reproducible identification of synteny blocks and chromosomal rearrangments via comparison of two genetic maps. syntR implements an error-aware clustering algorithm specifically designed for the highly linear structure of comparative genetic map data. syntR can be used to identify synteny blocks using any type of ordered genetic markers.&lt;/p&gt;
&lt;p&gt;The documentation and tutorial for syntR can be found here](https://ksamuk.github.io/syntR/index.html&lt;/p&gt;
&lt;p&gt;You can install syntR from Github with:&lt;/p&gt;
&lt;p&gt;``` r&lt;br&gt;install.packages("devtools")&lt;br&gt;devtools::install_github("ksamuk/syntR")&lt;br&gt;```&lt;/p&gt;
&lt;p&gt;Also see the README file. &lt;/p&gt;
description: <b>Dryad version number:</b> 5
<b>Version status:</b> submitted
<b>Dryad curation status:</b> Published
<b>Sharing link:</b> http://datadryad.org/dataset/doi:10.5061/dryad.7sqv9s4pc
<b>Storage size:</b> 5436610
<b>Visibility:</b> public
DOI: 10.15468/dl.ww4zx8
title: Occurrence Download

description: A dataset containing 110658 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lagopus muta (Montin, 1781)"
 ]
}

 The dataset includes 110658 records from 386 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007050-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15746439
title: CNN CaO
DOI: 10.5281/zenodo.15746438
title: CNN CaO
DOI: 10.5281/zenodo.8296554
title: UFLUX 100m half-yearly carbon, water, and energy fluxes in Europe in 2018

description: UFLUX Ensemble Europe100m6monthly (European 100 6-monthly) in 2018

OverviewThe UFLUX ensemble dataset offers European fluxes at 100 m spatial resolution, generated using Deep Forest machine learning models. It integrates satellite-based Sentinel-2 vegetation proxies NIRv with ERA5 climate reanalysis, and is trained against ICOS eddy covariance observations. The UFLUX project includes five core flux components:





Gross Primary Production (GPP)




Ecosystem Respiration (RECO)




Net Ecosystem Exchange (NEE)




Sensible Heat Flux (H)




Latent Energy Flux (LE)



Background and MethodologyThe Unified FLUXes (UFLUX) initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change.

Key innovations of UFLUX include:





Consistent Upscaling Framework: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data.




Hybrid Explainable ML: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2).




Uncertainty Quantification: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner.




Multisource Integration: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches.




Superior Gap-Filling: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods.




High Performance: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop.




Community Adoption: Already used by other global upscaling projects, highlighting its reliability and impact.



ApplicationsUFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements.

Resources



UFLUX Website: https://sites.google.com/view/uflux



Code Repository: https://github.com/soonyenju/uflux




Technical & Descriptive Publication: https://doi.org/10.1080/01431161.2024.2312266
DOI: 10.5281/zenodo.8296555
title: UFLUX 100m half-yearly carbon, water, and energy fluxes in Europe in 2018

description: UFLUX Ensemble Europe100m6monthly (European 100 6-monthly) in 2018

OverviewThe UFLUX ensemble dataset offers European fluxes at 100 m spatial resolution, generated using Deep Forest machine learning models. It integrates satellite-based Sentinel-2 vegetation proxies NIRv with ERA5 climate reanalysis, and is trained against ICOS eddy covariance observations. The UFLUX project includes five core flux components:





Gross Primary Production (GPP)




Ecosystem Respiration (RECO)




Net Ecosystem Exchange (NEE)




Sensible Heat Flux (H)




Latent Energy Flux (LE)



Background and MethodologyThe Unified FLUXes (UFLUX) initiative is a data-driven, machine learning-based platform designed to upscale eddy covariance (EC) flux measurements from tower sites to the global scale. It aims to answer pressing questions about how effectively terrestrial ecosystems are managed under climate change.

Key innovations of UFLUX include:





Consistent Upscaling Framework: Harmonizes flux upscaling across spatial/temporal scales and multiple flux types (GPP, RECO, etc.) using deep decision tree-based methods, better suited than conventional neural networks for EC flux data.




Hybrid Explainable ML: Combines black-box ML with ecological interpretability through residual learning, offering both predictive power and new scientific insight (UFLUXv2).




Uncertainty Quantification: Employs sampling space completeness to assess model uncertainty in a transparent, robust manner.




Multisource Integration: Leverages complementary strengths of vegetation proxies (e.g., NIRv, SIF) and climate data (e.g., ERA5) to represent carbon dynamics more comprehensively than single-source approaches.




Superior Gap-Filling: Originally developed as a global EC flux gap-filling tool, UFLUX improves accuracy by up to 30% and reduces uncertainty by as much as 70% compared to traditional methods.




High Performance: Achieves strong predictive accuracy, with global-scale R² > 0.8 for RECO and ≈0.9 for GPP, while being computationally efficient enough to run on a standard laptop.




Community Adoption: Already used by other global upscaling projects, highlighting its reliability and impact.



ApplicationsUFLUX is ideal for studying the interactions between land management, climate change, and carbon fluxes, particularly in improving global estimates of GPP and RECO by addressing biases in EC measurements.

Resources



UFLUX Website: https://sites.google.com/view/uflux



Code Repository: https://github.com/soonyenju/uflux




Technical & Descriptive Publication: https://doi.org/10.1080/01431161.2024.2312266
DOI: 10.15156/bio/sh1047864.10fu
title: SH1047864.10FU

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/sh1047865.10fu
title: SH1047865.10FU

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/sh1047866.10fu
title: SH1047866.10FU

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.