cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.m372b8
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((111.641 2.972174, 111.570643 2.966605, 111.502021 2.950036, 111.436825 2.922873, 111.376661 2.885789, 111.323011 2.839695, 111.277198 2.785729, 111.240348 2.72522, 111.213369 2.659658, 111.196922 2.590659, 111.19141 2.519922, 111.196969 2.449189, 111.21346 2.380201, 111.240474 2.314656, 111.277346 2.254168, 111.323166 2.200225, 111.376808 2.154155, 111.43695 2.117092, 111.502112 2.089947, 111.570691 2.073388, 111.641 2.067823, 111.711309 2.073388, 111.779888 2.089947, 111.84505 2.117092, 111.905192 2.154155, 111.958834 2.200225, 112.004654 2.254168, 112.041526 2.314656, 112.06854 2.380201, 112.085031 2.449189, 112.09059 2.519922, 112.085078 2.590659, 112.068631 2.659658, 112.041652 2.72522, 112.004802 2.785729, 111.958989 2.839695, 111.905339 2.885789, 111.845175 2.922873, 111.779979 2.950036, 111.711357 2.966605, 111.641 2.972174))"},{"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 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044682-251120083545085/datasets/export for details.
DOI: 10.5281/zenodo.17879669
title: Data associated with "Extracting and evaluating plant trait information from digital text in the Global Biodiversity Information Facility (GBIF)"

description: The data associated with Katori and Oguro (2025) (https://doi.org/10.1101/2025.03.16.643436):

A raw table presenting the all occurrence data on vascular plants downloaded from GBIF, containing the five columns used in this study as well as latitude and longitude data (File 1); Text and values of life span (LS), growth form (GF), flower color (FC), and plant height (PH), latitude, and longitude extracted from each occurrence of GBIF (File 2-4); A species-level dataset of LS, GF, FC, PHmax for five databases (GBIF, TRY, BIEN, eFloras, and Rice database)(File 5).

For detailed descriptions of each file and how to create the data, see "README.txt" and Katori and Oguro (2025) (https://doi.org/10.1101/2025.03.16.643436).
DOI: 10.15468/xfauqd
title: A new genus and two new species of Stevardiinae (Characiformes: Characidae) with a hypothesis on their relationships based on morphological and histological data

description: This dataset contains the digitized treatments in Plazi based on the original journal article Ferreira, Katiane M., Menezes, Naércio A., Quagio-Grassioto, Irani (2011): A new genus and two new species of Stevardiinae (Characiformes: Characidae) with a hypothesis on their relationships based on morphological and histological data. Neotropical Ichthyology 9 (2): 281-298, DOI: 10.1590/S1679-62252011000200005, URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1679-62252011000200005&lng=en&tlng=en
DOI: 10.15468/dl.3vwdkp
title: Occurrence Download

description: A dataset containing 4916 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Vipera latastei"
 ]
}

 The dataset includes 4916 records from 58 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044684-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kmha8g
title: Occurrence Download

description: A dataset containing 8664 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lophophanes cristatus",
 "Country is Czechia",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 8664 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044683-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.60893/figshare.pof.30640007
title: Supplement Number 1: Fitted Polynomial Coefficients for Expansion and Collapse Phases using DWO method

description: Polynomial coefficients ($a_{0}$--$a_{5}$) with standard deviations ($\sigma$), together with the initial conditions ($R_{0}$, $\dot{R}_0$), collapse radius ($R_{c}$), and RMSE values, reported separately for expansion and collapse phases across hypobaric conditions ($p_{\infty}$) and collapse cycles.
DOI: 10.60893/figshare.pof.30640007.v1
title: Supplement Number 1: Fitted Polynomial Coefficients for Expansion and Collapse Phases using DWO method

description: Polynomial coefficients ($a_{0}$--$a_{5}$) with standard deviations ($\sigma$), together with the initial conditions ($R_{0}$, $\dot{R}_0$), collapse radius ($R_{c}$), and RMSE values, reported separately for expansion and collapse phases across hypobaric conditions ($p_{\infty}$) and collapse cycles.
DOI: 10.15129/84502887-d283-4b05-bad6-2e9900bd3f57
title: Data for "Low-Cost 3D Printed Optics for Super-Resolution Multifocal Structured Illumination Microscopy"

description: Dataset for "Low-Cost 3D Printed Optics for Super-Resolution Multifocal Structured Illumination Microscopy." Contains image files and raw data from image and stylus profilometer data. Created for the purpose of communication of methods and results.
DOI: 10.57745/9fre3s
title: Core body temperature – Dataset description.docx

description: Short description of the experimental conditions, data identifiers, and column formats in the raw core body temperature dataset.
DOI: 10.57745/lw8p1v
title: Core body temperature_raw data.tab

description: Core body temperature -raw data
DOI: 10.57745/tm2vjk
title: Core body temperature_metadata.tab

description: :unav
DOI: 10.57745/evzdds
title: File 2 - Core body temperature_raw data.tab

description: Core body temperature -raw data
DOI: 10.57745/f9cuxr
title: Core body temperature – Metadata_Rdata-file description.docx

description: Short description of the experimental conditions, data identifiers, and column formats in the core body temperature_metada R file
DOI: 10.57745/vzttf1
title: File 1 - Core body temperature_metadata.tab

description: File describing the main characteristics of the animals.
DOI: 10.57745/pt8oqg
title: File- 0 - metadata.docx

description: Files containing the main characteristics of the trials, along with a full description of the variables, corresponding to the files ‘File 1 – Core body temperature_metadata.tab’ and ‘File 2 – Core body temperature_raw data.tab’.”
DOI: 10.57745/qofdnc
title: BT_animal_open_space_ suppl_figuresv1.docx

description: Supplementary figures accompanying the manuscript.
DOI: 10.5281/zenodo.17847677
title: Source Data for the publication: "First-principles modeling reveals energy flow in Photosystem II under low and high-light conditions"

description: refined_structure_PSII.pdb contains the refined structure employed for QM/MMPol calculations."Hamiltonian.csv" contains the full exciton Hamiltonian for the 318-pigments aggregate of PSII.

The other files contain data underlying figures in the associated paper (see 10.26434/chemrxiv-2025-blzwd):

Fig.1b: sim_spectrum_300K.csvFig.1c: sim_decay.npy ; t_ps_sim_decay.npyFig.2b: domains_pop.csv ; chls_pop.csvFig.2c: protein_pop.csv ; chls_pop.csvFig.2e: CP29_isolated.csv Fig.2f: CP29_in_PSII.csvFig.3a: Flows_Fig3a.csvFig.3b: Flows_Fig3b.csvFig.3c,d,e: MigrTimes_Partition.csvFig.4a: RelFreeEnergies.csvFig.5b: points_NPQ.csv
DOI: 10.15468/dl.vkh7fx
title: Occurrence Download

description: A dataset containing 6441649 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "CoordinateUncertaintyInMeters is less than or equal to 500.0",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Tracheophyta",
 "Year 1981-1990"
 ]
}

 The dataset includes 6441649 records from 749 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044654-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/14091246
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.5281/zenodo.17879791
title: Table 1 in Trichomycterus payaya, new catfish from headwaters of rio (Siluriformes: Trichomycteridae) Itapicuru, Bahia, Brazil

description: Table 1. Morphometric data of holotype and paratypes of Trichomycterus payaya (n = 15). The ranges include the holotype. SD = Standard deviation.
HolotypeRangeMeanSDStandard length (mm)37.429.2-37.432.7-Total length (mm)43.733.0-43.738.0-Percents of Standard lengthBody depth13.913.2-16.314.41.12Body width10.710.0-14.011.61.21Caudal peduncle depth9.17.5-10.28.80.79Caudal peduncle length19.517.4-22.219.71.18Predorsal length67.664.9-68.566.91.11Preanal length69.366.8-71.868.81.39Prepelvic length58.353.5-58.354.81.81Dorsal-fin base length9.99.9-12.911.40.85Anal-fin base length8.37.8-10.79.20.88Dorsal-caudal length12.17.6-12.510.31.53Nasal barbel length14.212.9-15.914.80.93Maxillary barbel length16.613.8-19.116.31.26Rictal barbel length13.112.6-15.813.70.91Head length20.319.7-22.421.10.81Percents of Head lengthHead width81.678.1-82.980.81.44Head depth42.137.0-43.140.11.85Inteorbital distance25.025.0-29.327.31.52Internarial distance15.811.9-15.814.41.25Internarial width21.421.1-27.324.32.01Snout length35.532.8-38.835.81.74Orbital diameter11.89.6-12.311.20.84Mouth width36.835.6-40.938.41.50
description: Published as part of Sarmento-Soares, Luisa M., Zanata, Angela M. & Martins-Pinheiro, Ronaldo F., 2011, Trichomycterus payaya, new catfish from headwaters of rio (Siluriformes: Trichomycteridae) Itapicuru, Bahia, Brazil, pp. 264 in Neotropical Ichthyology 9 (2) on page 264, DOI: 10.1590/S1679-62252011000200003, http://zenodo.org/record/4647524
DOI: 10.5281/zenodo.17879792
title: Table 1 in Trichomycterus payaya, new catfish from headwaters of rio (Siluriformes: Trichomycteridae) Itapicuru, Bahia, Brazil

description: Table 1. Morphometric data of holotype and paratypes of Trichomycterus payaya (n = 15). The ranges include the holotype. SD = Standard deviation.
HolotypeRangeMeanSDStandard length (mm)37.429.2-37.432.7-Total length (mm)43.733.0-43.738.0-Percents of Standard lengthBody depth13.913.2-16.314.41.12Body width10.710.0-14.011.61.21Caudal peduncle depth9.17.5-10.28.80.79Caudal peduncle length19.517.4-22.219.71.18Predorsal length67.664.9-68.566.91.11Preanal length69.366.8-71.868.81.39Prepelvic length58.353.5-58.354.81.81Dorsal-fin base length9.99.9-12.911.40.85Anal-fin base length8.37.8-10.79.20.88Dorsal-caudal length12.17.6-12.510.31.53Nasal barbel length14.212.9-15.914.80.93Maxillary barbel length16.613.8-19.116.31.26Rictal barbel length13.112.6-15.813.70.91Head length20.319.7-22.421.10.81Percents of Head lengthHead width81.678.1-82.980.81.44Head depth42.137.0-43.140.11.85Inteorbital distance25.025.0-29.327.31.52Internarial distance15.811.9-15.814.41.25Internarial width21.421.1-27.324.32.01Snout length35.532.8-38.835.81.74Orbital diameter11.89.6-12.311.20.84Mouth width36.835.6-40.938.41.50
description: Published as part of Sarmento-Soares, Luisa M., Zanata, Angela M. & Martins-Pinheiro, Ronaldo F., 2011, Trichomycterus payaya, new catfish from headwaters of rio (Siluriformes: Trichomycteridae) Itapicuru, Bahia, Brazil, pp. 264 in Neotropical Ichthyology 9 (2) on page 264, DOI: 10.1590/S1679-62252011000200003, http://zenodo.org/record/4647524
DOI: 10.5281/zenodo.17814764
title: Combined_HHI_IT_in_aBCC

description: This dataset contains processed count matrices and H&E images generated during our project investigating the effects of combined sonidegib and cemiplimab treatment in advanced basal cell carcinoma.

Three patients' (rqb506_Pat1, icf063_Pat2 and ptw592_Pat3) spatial transcriptomics data was generated using the 10x Visium platform. Supplied here are the filtered matrix outputs of the spaceranger (v2.1.1) pipeline, and the corresponding H&E images (_spatial.zip files). Besides the transcriptome, the proteome was also assayed using CITE-Seq. Each plate contains multiple biopsies of different timepoints. Baselines refer to sampling prior to the start of the combination treatment, V3-13 are visits (2weeks to 26 weeks) after treatment initiation.

Processed matrices output by Parse Biosciences' Trailmaker pipeline (two separate runs) are accompanied by the corresponding loading tables (metadata input required for the Trailmaker pipeline). The processed matrices are Seurat objects.
DOI: 10.5281/zenodo.17814765
title: Combined_HHI_IT_in_aBCC

description: This dataset contains processed count matrices and H&E images generated during our project investigating the effects of combined sonidegib and cemiplimab treatment in advanced basal cell carcinoma.

Three patients' (rqb506_Pat1, icf063_Pat2 and ptw592_Pat3) spatial transcriptomics data was generated using the 10x Visium platform. Supplied here are the filtered matrix outputs of the spaceranger (v2.1.1) pipeline, and the corresponding H&E images (_spatial.zip files). Besides the transcriptome, the proteome was also assayed using CITE-Seq. Each plate contains multiple biopsies of different timepoints. Baselines refer to sampling prior to the start of the combination treatment, V3-13 are visits (2weeks to 26 weeks) after treatment initiation.

Processed matrices output by Parse Biosciences' Trailmaker pipeline (two separate runs) are accompanied by the corresponding loading tables (metadata input required for the Trailmaker pipeline). The processed matrices are Seurat objects.
DOI: 10.4121/7408b922-3916-4cf2-9fad-afcbabb08bb4
title: Data for paper "Evaluation of Graph Neural Networks for Urban Drainage Metamodeling: Key Components and Transferability Analysis"

description: This dataset supports the article “Evaluation of Graph Neural Networks for Urban Drainage Metamodeling: Key Components and Transferability Analysis” (Garzón et al., 2025). It contains SWMM 5.1.015 simulation data and machine learning artifacts used to develop, train, and evaluate a Graph Neural Network based metamodel for two urban drainage systems: Loenen and Tuindorp. The data was produced as part of a PhD project at TU Delft to assess metamodel accuracy, speed up, data efficiency, and transferability under different conditions.<br>The folder "data" includes SWMM network files (.inp) and simulation outputs (lateral inflow, water levels, flows, and .out files) for training, validation, and testing. Testing included Dry Weather Flow conditions. Loenen includes complete training, validation, and testing sets. Tuindorp includes previously published training and validation data and newly added DWF testing data. All simulations were run using dynamic wave routing and rainfall events from the original case studies. No personal or sensitive data is included.<br>The folder "saved_objects" contains the machine learning components used in the study: pre trained model weights in PyTorch, feature normalizers used for consistent preprocessing, and pre processed time window datasets stored as PyTorch Geometric Data objects. These artifacts allow full reproduction of the metamodeling experiments and support transfer learning and further research.<br>The dataset enables researchers to replicate the methodology used in the paper, which combines SWMM hydrodynamic simulations with graph based deep learning models such as GINE and GAT trained on graph structured time series data. The workflow includes rainfall event simulation, feature normalization, graph construction, supervised learning, and evaluation on unseen inflow conditions including DWF baselines. <br>This work is supported by the TU Delft AI Labs programme.
DOI: 10.4121/7408b922-3916-4cf2-9fad-afcbabb08bb4.v1
title: Data for paper "Evaluation of Graph Neural Networks for Urban Drainage Metamodeling: Key Components and Transferability Analysis"

description: This dataset supports the article “Evaluation of Graph Neural Networks for Urban Drainage Metamodeling: Key Components and Transferability Analysis” (Garzón et al., 2025). It contains SWMM 5.1.015 simulation data and machine learning artifacts used to develop, train, and evaluate a Graph Neural Network based metamodel for two urban drainage systems: Loenen and Tuindorp. The data was produced as part of a PhD project at TU Delft to assess metamodel accuracy, speed up, data efficiency, and transferability under different conditions.<br>The folder "data" includes SWMM network files (.inp) and simulation outputs (lateral inflow, water levels, flows, and .out files) for training, validation, and testing. Testing included Dry Weather Flow conditions. Loenen includes complete training, validation, and testing sets. Tuindorp includes previously published training and validation data and newly added DWF testing data. All simulations were run using dynamic wave routing and rainfall events from the original case studies. No personal or sensitive data is included.<br>The folder "saved_objects" contains the machine learning components used in the study: pre trained model weights in PyTorch, feature normalizers used for consistent preprocessing, and pre processed time window datasets stored as PyTorch Geometric Data objects. These artifacts allow full reproduction of the metamodeling experiments and support transfer learning and further research.<br>The dataset enables researchers to replicate the methodology used in the paper, which combines SWMM hydrodynamic simulations with graph based deep learning models such as GINE and GAT trained on graph structured time series data. The workflow includes rainfall event simulation, feature normalization, graph construction, supervised learning, and evaluation on unseen inflow conditions including DWF baselines. <br>This work is supported by the TU Delft AI Labs programme.
DOI: 10.15468/dl.992hpd
title: Occurrence Download

description: A dataset containing 122 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Syllis alosae",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 122 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044687-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17877391
title: Highest Quality Remote Sensing Reflectance Database Compiled from 20+ years of MODIS-Aqua measurements

description: This database provides rigorously screened, highest-quality remote sensing reflectance (Rrs), spectra from the MODIS Aqua sensor at 4 km spatial resolution, covering seven wavelengths (412 nm, 443 nm, 488 nm, 531 nm, 547 nm, 667 nm, and 678 nm). It includes both 8-day and monthly composite products. The data are screened using CHQR (Criteria of the Highest-Quality Rrs) to have the selected Rrs spectrum meet the following conditions simultaneously:

l  Optimal observation conditions (excluding clouds, sun glints, etc.)

l  Spectral consistency with true water optical properties

l  Minimal residual uncertainties
DOI: 10.5281/zenodo.17877392
title: Highest Quality Remote Sensing Reflectance Database Compiled from 20+ years of MODIS-Aqua measurements

description: This database provides rigorously screened, highest-quality remote sensing reflectance (Rrs), spectra from the MODIS Aqua sensor at 4 km spatial resolution, covering seven wavelengths (412 nm, 443 nm, 488 nm, 531 nm, 547 nm, 667 nm, and 678 nm). It includes both 8-day and monthly composite products. The data are screened using CHQR (Criteria of the Highest-Quality Rrs) to have the selected Rrs spectrum meet the following conditions simultaneously:

l  Optimal observation conditions (excluding clouds, sun glints, etc.)

l  Spectral consistency with true water optical properties

l  Minimal residual uncertainties
DOI: 10.21942/uva.26318569
title: METARECORD: Boosting Behavioral Adaptability to Enhance Older Adults' Mental Health/Well-being and Quality of Life Using a Habit-Based Metacognitive Self-Help Intervention

description: The datasets generated and/or analyzed during the current study are not yet available as additional analyses covering different aims and hypotheses are still being performed. Once these analyses are completed, the data will be made available.
DOI: 10.21942/uva.26318569.v2
title: METARECORD: Boosting Behavioral Adaptability to Enhance Older Adults' Mental Health/Well-being and Quality of Life Using a Habit-Based Metacognitive Self-Help Intervention

description: The datasets generated and/or analyzed during the current study are not yet available as additional analyses covering different aims and hypotheses are still being performed. Once these analyses are completed, the data will be made available.
DOI: 10.21942/uva.26318569.v1
title: METARECORD: Boosting Resilience of Older Adults Using a Habit-Based Metacognitive Self-Help Intervention (MCSI)

description: The datasets generated and/or analyzed during the current study are not yet available as additional analyses covering different aims and hypotheses are still being performed. Once these analyses are completed, the data will be made available.
DOI: 10.6084/m9.figshare.30849353
title: OBS Waveform Data and Catalog from IRIS

description: We used ocean-bottom seismometer (OBS) waveform data from the South China Sea, merged with the global earthquake catalogs from the Incorporated Research Institutions for Seismology (IRIS) for 2017 and 2023, in this study. The results are presented in the manuscript “Seismicity distribution reveals inhomogeneous lithospheric rheology in the Southwest Sub-basin of the South China Sea,” which has been submitted to Seismological Research Letters.
DOI: 10.6084/m9.figshare.30849353.v1
title: OBS Waveform Data and Catalog from IRIS

description: We used ocean-bottom seismometer (OBS) waveform data from the South China Sea, merged with the global earthquake catalogs from the Incorporated Research Institutions for Seismology (IRIS) for 2017 and 2023, in this study. The results are presented in the manuscript “Seismicity distribution reveals inhomogeneous lithospheric rheology in the Southwest Sub-basin of the South China Sea,” which has been submitted to Seismological Research Letters.
DOI: 10.48320/4dcd595e-e063-4861-9ef5-9f8fd96008fe
title: Ukraine: Survey on the war in Ukraine 2022
DOI: 10.57702/m7p46h1j
title: silbiger-2019-1-1

description: IsSupplementTo: [TODO]; IsSourceOf: [TODO]
DOI: 10.5281/zenodo.17879941
title: Ukraine: Survey on the war in Ukraine 2022

description: The main objective of this research was to study the attitudes of Georgian population towards war in Ukraine. The survey examined who Georgians blame for the war, what they want the government to do, and the domestic political fallout of the government's response.

Population: Adult population (18 years old and over), excluding the populations living in territories affected by military conflict (South Ossetia and Abkhazia). Besides nationwide, data is representative of Tbilisi, urban and rural areas of Georgia.

Sample Design: Simple random sampling using random-digit-dial method.

Sample Size: 1,092 respondents.

Response rate: 24%

Survey mode: Computer-assisted telephone interview (CATI).

Interview languages: Georgian, Armenian, Azerbaijani and Russian.

Fieldwork Dates: March 7, 2022 to March 10, 2022.

Results are weighted.
description: Published on Discuss Data, https://discuss-data.net/dataset/4dcd595e-e063-4861-9ef5-9f8fd96008fe/
description: Method(s) of data collection: Cati (Computer-Assisted Telephone Interviews), Quantitative Survey