cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5061/dryad.6c9b2v5
title: Data from: Strength of sexual and postmating prezygotic barriers varies between sympatric populations with different histories and species abundances

description: The impact of different reproductive barriers on species or population
 isolation may vary in different stages of speciation depending on
 evolutionary forces acting within species and through species’
 interactions. Genetic incompatibilities between interacting species are
 expected to reinforce prezygotic barriers in sympatric populations and
 lead to cascade reinforcement between conspecific populations living
 within and outside the areas of sympatry. We tested these predictions and
 studied whether and how the strength and target of reinforcement between
 Drosophila montana and Drosophila flavomontana vary between sympatric
 populations with different histories and species abundances. All barriers
 between D. montana females and D. flavomontana males were nearly complete,
 while in the reciprocal cross strong postzygotic isolation was accompanied
 by prezygotic barriers whose strength varied according to population
 composition. Sexual isolation between D. flavomontana females and D.
 montana males was increased in long-established sympatric populations,
 where D. flavomontana is abundant, while postmating prezygotic (PMPZ)
 barriers were stronger in populations where this species is a new invader
 and still rare and where female discrimination against heterospecific
 males was lower. Strengthening of sexual and PMPZ barriers in this cross
 also induced cascade reinforcement of respective barriers between D.
 flavomontana populations, which is a classic signature of reinforcement
 process.
description: Data from: Strength of
 sexual and postmating prezygotic barriers varies between sympatric
 populations with different histories and species
 abundancesReproductive_barriers_Dmontana_Dflavomontana.xlsx
DOI: 10.5061/dryad.k08pc1r
title: Data from: Ploeotids represent much of the phylogenetic diversity of euglenids

description: Ploeotids are an assemblage of rigid phagotrophic euglenids that have
 10–12 pellicular strips and glide on their posterior flagellum. Molecular
 phylogenies place them as a poorly resolved, likely paraphyletic
 assemblage outside the Spirocuta clade of flexible euglenids, which
 includes the well-known phototrophs and primary osmotrophs. Here, we
 report SSU rRNA gene sequences from 38 ploeotids, using both single-cell
 and culture-based methods. Several contain group I or non-canonical
 introns. Our phylogenetic analyses place ploeotids in 8 distinct clades:
 Olkasia n. gen, Hemiolia n. gen Liburna n. gen, Lentomonas, Decastava,
 Keelungia, Ploeotiidae, and Entosiphon. Ploeotia vitrea, the type of
 Ploeotia, is closely related to P. oblonga and Serpenomonas costata, but
 not to Lentomonas. Ploeotia cf. vitrea sensu Lax and Simpson 2013 is not
 related to P. vitrea and has a different pellicle strip architecture (as
 imaged by scanning electron microscopy): it instead represents a novel
 genus and species, Olkasia polycarbonata. We also describe new genera,
 Hemiolia and Liburna, for the morphospecies Anisonema trepidum and A.
 glaciale. A recent system proposing 13 suprafamilial taxa that include
 ploeotids is not supported by our phylogenies. The exact relationships
 between ploeotid groups remain unresolved and multigene phylogenetics or
 phylogenomics are needed to address this uncertainty.
description: Cell measurements of
 culturesIndividual measurements
 of ploeotid cultures.cell_measurements_cultures.xlsxRaw SEM images of PloeotidsRaw Scanning Electron Microscopy (SEM) images of and Olkasia polycarbonata and other cultured ploeotid taxaraw_images_SEM.zipRaw treefiles of ploeotid phylogenetic analysesRaw treefiles (NEWICK-format and PDF) and trimmed alignment data (fasta-format) for phylogenetic analyses of ploeotids.treefiles_trimmed-alignments.zipUntrimmed datasets of main phylogenetic analyses of ploeotidsUntrimmed alignments (fasta-format) of phylogenetic analyses of ploeotids: 1) All taxa, including Entosiphon, 2) excluding Entosiphonuntrimmed_datasets.zipRaw LM images of ploeotidsRaw light microscopy (LM) images of isolated and cultured ploeotid taxa.raw_images_LM.zip
DOI: 10.5281/zenodo.18391470
title: THE IMPАCT ОF TYPE 1 DIАBETES MELLITUS ОN PHYSIОLОGICАL GRОWTH АND PUBERTАL DEVELОPMENT IN CHILDREN АND АDОLESCENTS

description: Type 1 Diаbetes Mellitus (T1DM) аdversely аffects the physiоlоgicаl grоwth аnd pubertаl develоpment оf children аnd аdоlescents due tо chrоnic hyperglycemiа аnd insulin deficiency. Pооr glycemic cоntrоl disrupts the GH-IGF-1 аxis, leаding tо grоwth delаys, reduced bоne density, аnd аn increаsed risk оf frаctures. Mоreоver, T1DM interferes with the hypоthаlаmic-pituitаry-gоnаdаl (HPG) аxis, cаusing delаyed puberty, hоrmоnаl imbаlаnces, аnd reprоductive heаlth cоmplicаtiоns. Girls with T1DM оften experience lаte menаrche аnd оvаriаn dysfunctiоn, while bоys mаy hаve delаyed testiculаr develоpment аnd grоwth retаrdаtiоn. Effective diаbetes mаnаgement, including strict glycemic cоntrоl аnd eаrly interventiоn, is essentiаl tо mitigаte these аdverse effects аnd imprоve оverаll develоpmentаl оutcоmes.
DOI: 10.5281/zenodo.18391469
title: THE IMPАCT ОF TYPE 1 DIАBETES MELLITUS ОN PHYSIОLОGICАL GRОWTH АND PUBERTАL DEVELОPMENT IN CHILDREN АND АDОLESCENTS

description: Type 1 Diаbetes Mellitus (T1DM) аdversely аffects the physiоlоgicаl grоwth аnd pubertаl develоpment оf children аnd аdоlescents due tо chrоnic hyperglycemiа аnd insulin deficiency. Pооr glycemic cоntrоl disrupts the GH-IGF-1 аxis, leаding tо grоwth delаys, reduced bоne density, аnd аn increаsed risk оf frаctures. Mоreоver, T1DM interferes with the hypоthаlаmic-pituitаry-gоnаdаl (HPG) аxis, cаusing delаyed puberty, hоrmоnаl imbаlаnces, аnd reprоductive heаlth cоmplicаtiоns. Girls with T1DM оften experience lаte menаrche аnd оvаriаn dysfunctiоn, while bоys mаy hаve delаyed testiculаr develоpment аnd grоwth retаrdаtiоn. Effective diаbetes mаnаgement, including strict glycemic cоntrоl аnd eаrly interventiоn, is essentiаl tо mitigаte these аdverse effects аnd imprоve оverаll develоpmentаl оutcоmes.
DOI: 10.15468/dl.7afmu2
title: Occurrence Download

description: A dataset containing 38484427 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Colombia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 38484427 records from 4633 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001612-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uqdt9z
title: Occurrence Download

description: A dataset containing 156 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((126.96117 26.01942,119.89834 45.51278,102.94759 45.23028,101.53503 17.54403,110.85794 13.30636,123.57101 12.1763,126.96117 26.01942))",
 "TaxonKey is Zelkova sinica"
 ]
}

 The dataset includes 156 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032335-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5usyn9
title: Occurrence Download

description: A dataset containing 175 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Geometry POLYGON((50.97879 -11.79899,150.02121 -11.79899,150.02121 27.79899,50.97879 27.79899,50.97879 -11.79899))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Tupaia tana"
 ]
}

 The dataset includes 175 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032336-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17605368
title: Data of "Cloud Condensation Nuclei and the Contribution of New Particle Formation in the Yangtze River Delta of Eastern China" -version1

description: Cloud condensation nuclei (CCN) serve as a critical link between aerosols and clouds, with new particle formation (NPF) recognized as a major source of CCN. However, the quantitative contribution of NPF to CCN remains highly variable and insufficiently constrained by in-situ observations. To address this gap, two-year synchronized observations of CCN, particle number size distribution (PNSD), and chemical composition were conducted in an urban area of eastern China. The average CCN concentration (NCCN) was 2220 ± 1050, 3510 ± 1500, 4050 ± 1640, and 4400 ± 1740 cm⁻³ at 0.2%, 0.4%, 0.6%, and 0.8% supersaturation, respectively. Comparisons between observed and calculated NCCN derived from PNSD and aerosol hygroscopicity revealed uncertainties in representing the hygroscopicity of organic-rich and NPF-origin particles. NCCN exhibited distinct diurnal variations, with concurrent anthropogenic emissions obscuring the apparent NPF contribution to CCN. By distinguishing CCN into primary and secondary components and focusing on secondary CCN, NPF-related increases were estimated at 54-71% across supersaturations of 0.2-0.8%, substantially higher than conventional estimates. Results from the complementary survival probability (SP) approach further confirmed enhanced CCN formation efficiency under warm, low-pollution conditions, consistent with higher formation rates and SP. As air quality management continues to reduce PM2.5 mass concentrations, the organic fraction and NPF frequency are likely to increase. These trends underscore the growing importance of NPF in CCN production and the necessity for coordinated, long-term observations of CCN and NPF to improve constraints on aerosol–cloud interactions and climate effects.
DOI: 10.5281/zenodo.17605367
title: Data of "Cloud Condensation Nuclei and the Contribution of New Particle Formation in the Yangtze River Delta of Eastern China" -version1

description: Cloud condensation nuclei (CCN) serve as a critical link between aerosols and clouds, with new particle formation (NPF) recognized as a major source of CCN. However, the quantitative contribution of NPF to CCN remains highly variable and insufficiently constrained by in-situ observations. To address this gap, two-year synchronized observations of CCN, particle number size distribution (PNSD), and chemical composition were conducted in an urban area of eastern China. The average CCN concentration (NCCN) was 2220 ± 1050, 3510 ± 1500, 4050 ± 1640, and 4400 ± 1740 cm⁻³ at 0.2%, 0.4%, 0.6%, and 0.8% supersaturation, respectively. Comparisons between observed and calculated NCCN derived from PNSD and aerosol hygroscopicity revealed uncertainties in representing the hygroscopicity of organic-rich and NPF-origin particles. NCCN exhibited distinct diurnal variations, with concurrent anthropogenic emissions obscuring the apparent NPF contribution to CCN. By distinguishing CCN into primary and secondary components and focusing on secondary CCN, NPF-related increases were estimated at 54-71% across supersaturations of 0.2-0.8%, substantially higher than conventional estimates. Results from the complementary survival probability (SP) approach further confirmed enhanced CCN formation efficiency under warm, low-pollution conditions, consistent with higher formation rates and SP. As air quality management continues to reduce PM2.5 mass concentrations, the organic fraction and NPF frequency are likely to increase. These trends underscore the growing importance of NPF in CCN production and the necessity for coordinated, long-term observations of CCN and NPF to improve constraints on aerosol–cloud interactions and climate effects.
DOI: 10.15468/dl.katx9b
title: Occurrence Download

description: A dataset containing 15273 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Sceloporus magister",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 15273 records from 59 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032337-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jygkda
title: Occurrence Download

description: A dataset containing 2665 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Sceloporus malachiticus",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 2665 records from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032338-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wz29d2
title: Occurrence Download

description: A dataset containing 454 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Poospiza rubecula"
 ]
}

 The dataset includes 454 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032339-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.afa4g2
title: Occurrence Download

description: A dataset containing 812 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Sceloporus megalepidurus",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 812 records from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032340-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15186333
title: Supplementary material for "Strain Rates along the Alpine-Himalayan Belt from a Comprehensive GNSS Velocity Field"

description: Supplementary material for "Strain Rates along the Alpine-Himalayan Belt from a Comprehensive GNSS Velocity Field"

Journal of Geophysical Research: Solid Earth

N. Castro-Perdomo et al., (2025)

Corresponding author: N. Castro-Perdomo, jcastrop@iu.edu 

The results and figures presented in our manuscript are fully reproducible. To replicate our analyses, run the Jupyter notebook JGR_Alpides_Supplements.ipynb included with this repository.

Instructions:

1. Set up a Conda environment (optional but recommended)

To manage dependencies and ensure reproducibility, we recommend creating a Conda environment using the provided environment.yml file. This step is optional if all required packages are already installed on your system.

To create and activate the Conda environment, run:

conda env create -f environment.yml
conda activate alpides


2. Launch the Jupyter notebook

Start the Jupyter notebook to reproduce the manuscript's results:

jupyter notebook JGR_Alpides_Supplements.ipynb


If alpides does not appear in the Jupyter notebook's kernel list, you can manually register it:

python -m ipykernel install --user --name alpides --display-name "Python (alpides)"


After registering the kernel, restart Jupyter Notebook to ensure the new kernel appears in the kernel list.

Directory structure

The diagram below outlines the contents and organization of the supplementary material provided with this repository.

📦Alpides_Strain_Rates_Supplements
 ┣ 📜JGR_Alpides_Supplements.ipynb
 ┣ 📂combined_velocity_field
 ┃ ┣ 📜aegean.csv
 ┃ ┣ 📜amuria.csv
 ┃ ┣ 📜anatolia.csv
 ┃ ┣ 📜arabia.csv
 ┃ ┣ 📜eurasia.csv
 ┃ ┣ 📜IGB14.csv
 ┃ ┣ 📜india.csv
 ┃ ┣ 📜nubia.csv
 ┃ ┣ 📜sinai.csv
 ┃ ┣ 📜tibet.csv
 ┃ ┣ 📜zangtze.csv
 ┃ ┗ 📜combined_vertical_velocity_field.vel
 ┣ 📂strain_rate_data
 ┃ ┣ 📜strain_rates_ALPIDES.csv
 ┃ ┣ 📜strain_rates_EMED_creep.csv
 ┃ ┣ 📜strain_rates_EMED_nocreep.csv
 ┃ ┣ 📜strain_rates_India_Asia.csv
 ┣ 📂input_data
 ┃ ┣ 📂cpts
 ┃ ┣ 📂datasets
 ┃ ┣ 📂modules
 ┣ 📂output_data
 ┃ ┣ 📂figures
 ┣ 📜LICENSE
 ┣ 📜README.md
 ┣ 📜environment.yml
 ┣ 📜requirements.txt ┣ 📜reproducible_research.html
 ┗ 📜runtime.txt




Combined GNSS velocities rotated into different reference frames are provided in the combined_velocity_field folder

Mean posterior strain rates and uncertainties are provided in the strain_rate_data folder

Ancillary files including fault traces, SKS data, and other supporting files can be found in the input_data folder

Python modules required for plotting strain rates, harmonizing GNSS velocity uncertainties and performing Metropolis MCMC inversions of GNSS velocities for fault kinematic parameters are included in the input_data/modules folder
DOI: 10.5281/zenodo.17605374
title: Supplementary material for "Strain Rates along the Alpine-Himalayan Belt from a Comprehensive GNSS Velocity Field"

description: Supplementary material for "Strain Rates along the Alpine-Himalayan Belt from a Comprehensive GNSS Velocity Field"

Journal of Geophysical Research: Solid Earth

N. Castro-Perdomo et al., (2025)

Corresponding author: N. Castro-Perdomo, jcastrop@iu.edu 

The results and figures presented in our manuscript are fully reproducible. To replicate our analyses, run the Jupyter notebook JGR_Alpides_Supplements.ipynb included with this repository.

Instructions:

1. Set up a Conda environment (optional but recommended)

To manage dependencies and ensure reproducibility, we recommend creating a Conda environment using the provided environment.yml file. This step is optional if all required packages are already installed on your system.

To create and activate the Conda environment, run:

conda env create -f environment.yml
conda activate alpides


2. Launch the Jupyter notebook

Start the Jupyter notebook to reproduce the manuscript's results:

jupyter notebook JGR_Alpides_Supplements.ipynb


If alpides does not appear in the Jupyter notebook's kernel list, you can manually register it:

python -m ipykernel install --user --name alpides --display-name "Python (alpides)"


After registering the kernel, restart Jupyter Notebook to ensure the new kernel appears in the kernel list.

Directory structure

The diagram below outlines the contents and organization of the supplementary material provided with this repository.

📦Alpides_Strain_Rates_Supplements
 ┣ 📜JGR_Alpides_Supplements.ipynb
 ┣ 📂combined_velocity_field
 ┃ ┣ 📜aegean.csv
 ┃ ┣ 📜amuria.csv
 ┃ ┣ 📜anatolia.csv
 ┃ ┣ 📜arabia.csv
 ┃ ┣ 📜eurasia.csv
 ┃ ┣ 📜IGB14.csv
 ┃ ┣ 📜india.csv
 ┃ ┣ 📜nubia.csv
 ┃ ┣ 📜sinai.csv
 ┃ ┣ 📜tibet.csv
 ┃ ┣ 📜zangtze.csv
 ┃ ┗ 📜combined_vertical_velocity_field.vel
 ┣ 📂strain_rate_data
 ┃ ┣ 📜strain_rates_ALPIDES.csv
 ┃ ┣ 📜strain_rates_EMED_creep.csv
 ┃ ┣ 📜strain_rates_EMED_nocreep.csv
 ┃ ┣ 📜strain_rates_India_Asia.csv
 ┣ 📂input_data
 ┃ ┣ 📂cpts
 ┃ ┣ 📂datasets
 ┃ ┣ 📂modules
 ┣ 📂output_data
 ┃ ┣ 📂figures
 ┣ 📜LICENSE
 ┣ 📜README.md
 ┣ 📜environment.yml
 ┣ 📜requirements.txt ┣ 📜reproducible_research.html
 ┗ 📜runtime.txt




Combined GNSS velocities rotated into different reference frames are provided in the combined_velocity_field folder

Mean posterior strain rates and uncertainties are provided in the strain_rate_data folder

Ancillary files including fault traces, SKS data, and other supporting files can be found in the input_data folder

Python modules required for plotting strain rates, harmonizing GNSS velocity uncertainties and performing Metropolis MCMC inversions of GNSS velocities for fault kinematic parameters are included in the input_data/modules folder
DOI: 10.15468/dl.b8ce82
title: Occurrence Download

description: A dataset containing 43918 species occurrences available in GBIF matching the query:
{
 "and" : [
 "SpeciesKey is Antheraea polyphemus",
 "HasCoordinate is True"
 ]
}

 The dataset includes 43918 records from 73 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032341-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8dy76v
title: Occurrence Download

description: A dataset containing 210 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((130.06879 37.60244,101.53503 44.10022,84.01924 27.43198,100.12247 7.65612,127.80862 8.50363,130.06879 37.60244))",
 "TaxonKey is Ulmus bergmanniana"
 ]
}

 The dataset includes 210 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032343-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.26275/hnxu-ifta
title: Human vagus nerve anatomical reconstruction using microCT and immunohistochemistry - f004

description: SPARC REVA FEINSTEIN
description: Data created as part of the SPARC REVA award. The dataset includes anatomical images and videos, microCT scans across the length of the nerve and IHC image across multiple levels.
DOI: 10.15468/dl.dc4q8r
title: Occurrence Download

description: A dataset containing 410577 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-105.2688 21.44913, -105.1188 21.44913, -105.1188 21.59913, -105.2688 21.59913, -105.2688 21.44913))"
 ]
}

 The dataset includes 410577 records from 237 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032312-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17602902
title: Nov2025 reference datasets for Mitohelper

description: Reference datasets (Nov 2025 update) for Mitohelper (https://github.com/aomlomics/mitohelper)



The MitoFish database underwent a major update since September 30th (UTC + 9) where duplicated records were combined into one entry. The Nov2025 version of Mitohelper's reference datasets was de-deduplicated based on updated records in the September 30th version of the MitoFish database.


Mitohelper is a repository built to facilitate experimental design, alignment visualization, and reference sequence analysis in fish eDNA studies. Refer to our paper and Mitohelper's wiki for database construction pipeline.

I. Reference database files in tab-separated format, containing gene, taxonomy, and sequence information:



mitofish.all.Nov.2025.tsv (558,313 de-duplicated records)

mitofish.12S.Mar2025.tsv (64,059 records of 12S rRNA gene >50 bp long)

mitofish.12S.Mar2025_NR.fasta (FASTA file of 12S rRNA gene records)

mitofish.COI.Mar2025.tsv (200,911 de-duplicated records)


II. De-replicated QIIME 2-compatible 12S/12S+16S+18S rRNA reference datasets:



12S-seqs-derep-uniq.qza

12S-tax-derep-uniq.qza

12S-16S-18S-seqs.qza

12S-16S-18S-tax.qza


If you use Mitohelper, please cite:Jean Lim, S, Thompson, LR. Mitohelper: A mitochondrial reference sequence analysis tool for fish eDNA studies. Environmental DNA. 2021; 00: 1– 10.  https://doi.org/10.1002/edn3.187