cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.49kzuj
title: Occurrence Download

description: A dataset containing 2233063 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Syedra gracilis (Menge, 1869), Sylvia borin (Boddaert, 1783), Sylvia communis Latham, 1787)",
 "Geometry POLYGON((-9.36035 44.71551,-10.59082 42.81152,-11.82129 36.80928,-7.69043 36.17336,-3.38379 35.88905,3.12012 38.13456,9.18457 38.20366,11.7334 37.16032,12.78809 34.74161,17.09473 33.79741, 23.15918 34.23451,30.10254 32.62087,33.2666 32.99024,35.72754 35.03,33.00293 35.53223,28.69629 35.17381,28.34473 36.31513,25.7959 37.50973,24.91699 39.90974,28.78418 40.6473,30.71777 41.17865,36.87012 45.213,38.18848 46.98025,38.71582 47.7541,39.94629 47.93107,40.20996 49.61071,37.0459 50.34546,35.72754 50.40152,33.79395 52.26816,31.68457 51.94426,31.4209 53.27835,32.56348 53.43572,30.98145 54.77535,30.89355 55.52863,28.43262 56.02295,27.20215 57.65716,27.37793 58.30949,27.90527 59.17593,29.66309 59.88894,27.6416 60.58697,31.50879 62.75473,28.78418 69.83962,30.89355 70.28912,29.22363 71.55274,21.75293 71.30079,14.28223 70.14036,11.38184 67.47492,4.87793 63.66576,-5.53711 71.52491,-10.63477 71.69129,-21.5332 67.74276,-28.56445 65.36684,-24.3457 61.35461,-9.36035 44.71551))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2021"
 ]
}

 The dataset includes 2233063 records from 1277 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0329047-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7483/opendata.cms.f52b.t5ad
title: /NMSSM_XToYHTo2Tau2B_MX-240_MY-80_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.n25o.23ck
title: /NMSSM_XToYHTo2Tau2B_MX-240_MY-80_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.wbyq.c30y
title: /NMSSM_XToYHTo2Tau2B_MX-240_MY-90_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.y2um.4x6a
title: /NMSSM_XToYHTo2Tau2B_MX-240_MY-90_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.9rvv.n9ob
title: /NMSSM_XToYHTo2Tau2B_MX-2500_MY-1000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.dlrl.8wli
title: /NMSSM_XToYHTo2Tau2B_MX-2500_MY-1000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.5281/zenodo.13863911
title: scGPT: End-to-End Protocol for Fine-tuned Retina Cell Type Annotation

description: Abstract

Single-cell research faces challenges in accurately annotating cell types at high resolution, especially when dealing with large-scale datasets and rare cell populations. To address this, foundation models like scGPT offer flexible, scalable solutions by leveraging transformer-based architectures. This protocol provides a comprehensive guide to fine-tuning scGPT for cell-type classification in single-cell RNA sequencing (scRNA-seq) data. We demonstrate how to fine-tune scGPT on a custom retina dataset, highlighting the model’s efficiency in handling complex data and improving annotation accuracy achieving 99.5% F1-score. This protocol automates key steps, including data preprocessing, model fine-tuning, and evaluation. This protocol enables researchers to efficiently deploy scGPT for their own datasets. The provided tools, including a command-line interface and Jupyter Notebook, simplify the customization and exploration of the model, offering an accessible workflow for users with basic Python and Linux knowledge. This protocol equips researchers with intermediate bioinformatics skills to enhance the precision and speed of cell-type annotations using scGPT. The source code and example datasets are publicly available on Github and Zenodo.
description: Data Description

TRAIN_snRNA2_9M.h5ad

Training dataset that is splitted from 9 : 1 ratio of original dataset. 

n_obs × n_vars = 1327511 × 36601

EVAL_snRNA_ac_enriched.h5ad

Evaluation dataset that is AC (Amacrine cell) enriched. 

n_obs × n_vars = 7070 × 36601

EVAL_snRNA_bc_enriched.h5ad

Evaluation dataset that is BC (Bipolar Cell) enriched. 

n_obs × n_vars = 27293 × 36601

EVAL_snRNA_rgc_enriched.h5ad

Evaluation dataset that is RGC (Retinal ganglion cell) enriched. 

n_obs × n_vars = 7681 × 36601

EVAL_snRNA_no_enriched.h5ad

Evaluation dataset without any enrichment. The majority of cells are ROD. 

n_obs × n_vars = 11977 × 36601

EVAL_snRNA_amd_sample1.h5ad

Evaluation dataset that is a Age-related Macular Degeneration (AMD) sample. 

n_obs × n_vars = 9583 × 36601

EVAL_snRNA_amd_sample2.h5ad

Evaluation dataset that is a Age-related Macular Degeneration (AMD) sample. 

n_obs × n_vars = 11471 × 36601

EVAL_BC_class.h5ad

Evaluation dataset that contains all BCs from all six evaluation datasets: no_enriched, ac_enriched, bc_enriched, rgc_enriched, and 2 AMD samples. 

n_obs × n_vars = 16167 × 36601
DOI: 10.5281/zenodo.13896150
title: scGPT: End-to-End Protocol for Fine-tuned Retina Cell Type Annotation

description: Abstract

Single-cell research faces challenges in accurately annotating cell types at high resolution, especially when dealing with large-scale datasets and rare cell populations. To address this, foundation models like scGPT offer flexible, scalable solutions by leveraging transformer-based architectures. This protocol provides a comprehensive guide to fine-tuning scGPT for cell-type classification in single-cell RNA sequencing (scRNA-seq) data. We demonstrate how to fine-tune scGPT on a custom retina dataset, highlighting the model’s efficiency in handling complex data and improving annotation accuracy achieving 99.5% F1-score. This protocol automates key steps, including data preprocessing, model fine-tuning, and evaluation. This protocol enables researchers to efficiently deploy scGPT for their own datasets. The provided tools, including a command-line script and Jupyter Notebook, simplify the customization and exploration of the model, proposing an accessible workflow for users with minimal Python and Linux knowledge. The protocol offers an off-the-shell solution of high-precision cell-type annotation using scGPT for researchers with intermediate bioinformatics.
description: File Description

TRAIN_snRNA2_9M.h5ad

Training dataset that is splitted from 9 : 1 ratio of original dataset. 

n_obs × n_vars = 1327511 × 36601

EVAL_snRNA_ac_enriched.h5ad

Evaluation dataset that is AC (Amacrine cell) enriched. 

n_obs × n_vars = 7070 × 36601

EVAL_snRNA_bc_enriched.h5ad

Evaluation dataset that is BC (Bipolar Cell) enriched. 

n_obs × n_vars = 27293 × 36601

EVAL_snRNA_rgc_enriched.h5ad

Evaluation dataset that is RGC (Retinal ganglion cell) enriched. 

n_obs × n_vars = 7681 × 36601

EVAL_snRNA_no_enriched.h5ad

Evaluation dataset without any enrichment. The majority of cells are ROD. 

n_obs × n_vars = 11977 × 36601

EVAL_snRNA_amd_sample1.h5ad

Evaluation dataset that is a Age-related Macular Degeneration (AMD) sample. 

n_obs × n_vars = 9583 × 36601

EVAL_snRNA_amd_sample2.h5ad

Evaluation dataset that is a Age-related Macular Degeneration (AMD) sample. 

n_obs × n_vars = 11471 × 36601

EVAL_BC_class.h5ad

Evaluation dataset that contains all BCs from all six evaluation datasets: no_enriched, ac_enriched, bc_enriched, rgc_enriched, and 2 AMD samples. 

n_obs × n_vars = 16167 × 36601

Fine-tuned Model

finetuned_AiO.zip

AiO stands for All-in-One. This zip file contains:



vocab.jsonGene vocabulary dictionary

id2type.jsonCell ID-to-type mapping

best_model.ptFine-tuned model file

dev_train_args.ymlModel configuraion file that can be inherited

run.logTraining logs for the model

README.mdImportant instructions if using this fine-tuned model
DOI: 10.7483/opendata.cms.pv52.9hg7
title: /NMSSM_XToYHTo2Tau2B_MX-2500_MY-100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.qpuj.sxte
title: /NMSSM_XToYHTo2Tau2B_MX-2500_MY-100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.s2ne.5nf0
title: /NMSSM_XToYHTo2Tau2B_MX-2500_MY-1100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.7e9l.5nxv
title: /NMSSM_XToYHTo2Tau2B_MX-2500_MY-1100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.76dg.lhw8
title: /NMSSM_XToYHTo2Tau2B_MX-2500_MY-1200_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.km17.h4dn
title: /NMSSM_XToYHTo2Tau2B_MX-2500_MY-1200_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.4yg6.jacf
title: /NMSSM_XToYHTo2Tau2B_MX-2500_MY-125_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.80vz.9a7y
title: /NMSSM_XToYHTo2Tau2B_MX-2500_MY-125_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.7snh.8fmg
title: /NMSSM_XToYHTo2Tau2B_MX-2500_MY-1300_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.y2c4.gnyu
title: /NMSSM_XToYHTo2Tau2B_MX-2500_MY-1300_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.9z1f.t47g
title: /NMSSM_XToYHTo2Tau2B_MX-2500_MY-1400_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.tmfo.rtfj
title: /NMSSM_XToYHTo2Tau2B_MX-2500_MY-1400_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.i44f.fhr2
title: /NMSSM_XToYHTo2Tau2B_MX-2500_MY-150_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.r69c.cmlq
title: /NMSSM_XToYHTo2Tau2B_MX-2500_MY-150_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.5xg8.rm4t
title: /NMSSM_XToYHTo2Tau2B_MX-2500_MY-1600_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.0zo4.69d3
title: /NMSSM_XToYHTo2Tau2B_MX-2500_MY-1600_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.rthw.hikh
title: /NMSSM_XToYHTo2Tau2B_MX-2500_MY-1800_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.nx8q.t9ve
title: /NMSSM_XToYHTo2Tau2B_MX-2500_MY-1800_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.hr70.7mx2
title: /NMSSM_XToYHTo2Tau2B_MX-2500_MY-2000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.w1be.dfu5
title: /NMSSM_XToYHTo2Tau2B_MX-2500_MY-2000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.qjk5.adlv
title: /NMSSM_XToYHTo2Tau2B_MX-2500_MY-2200_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.okse.e5gb
title: /NMSSM_XToYHTo2Tau2B_MX-2500_MY-2200_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.tg1s.z5mb
title: /NMSSM_XToYHTo2Tau2B_MX-2500_MY-250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.usx7.m0bx
title: /NMSSM_XToYHTo2Tau2B_MX-2500_MY-250_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.tygh.a38i
title: /NMSSM_XToYHTo2Tau2B_MX-2500_MY-300_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.15468/dl.9y7j87
title: Occurrence Download

description: A dataset containing 8796 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pterocarya fraxinifolia (Poir.) Spach",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 8796 records from 162 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069614-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7483/opendata.cms.c16z.fero
title: /NMSSM_XToYHTo2Tau2B_MX-2500_MY-300_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.ah9u.xl3q
title: /NMSSM_XToYHTo2Tau2B_MX-2500_MY-600_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.7p7f.03un
title: /NMSSM_XToYHTo2Tau2B_MX-2500_MY-600_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.fmk8.n1lo
title: /NMSSM_XToYHTo2Tau2B_MX-2500_MY-60_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.1e4q.cizp
title: /NMSSM_XToYHTo2Tau2B_MX-2500_MY-60_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.2kx9.nrml
title: /NMSSM_XToYHTo2Tau2B_MX-2500_MY-700_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.x92p.2hnh
title: /NMSSM_XToYHTo2Tau2B_MX-2500_MY-700_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.efrf.lqkz
title: /NMSSM_XToYHTo2Tau2B_MX-2500_MY-70_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.u0pt.0w7h
title: /NMSSM_XToYHTo2Tau2B_MX-2500_MY-70_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.5e70.5eax
title: /NMSSM_XToYHTo2Tau2B_MX-2500_MY-800_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.86g0.lhjk
title: /NMSSM_XToYHTo2Tau2B_MX-2500_MY-800_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.e5wk.8qt8
title: /NMSSM_XToYHTo2Tau2B_MX-2500_MY-80_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.uuru.x9kk
title: /NMSSM_XToYHTo2Tau2B_MX-2500_MY-80_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.knch.bprn
title: /NMSSM_XToYHTo2Tau2B_MX-2500_MY-900_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.wn7b.c5fy
title: /NMSSM_XToYHTo2Tau2B_MX-2500_MY-900_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.uoph.cxaj
title: /NMSSM_XToYHTo2Tau2B_MX-2500_MY-90_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.12yu.1626
title: /NMSSM_XToYHTo2Tau2B_MX-2500_MY-90_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.k6yq.lny1
title: /NMSSM_XToYHTo2Tau2B_MX-2600_MY-1000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.kfqc.0wed
title: /NMSSM_XToYHTo2Tau2B_MX-2600_MY-1000_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.7483/opendata.cms.j567.u13p
title: /NMSSM_XToYHTo2Tau2B_MX-2600_MY-100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.7483/opendata.cms.437o.akuh
title: /NMSSM_XToYHTo2Tau2B_MX-2600_MY-100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.15468/dl.useztk
title: Occurrence Download

description: A dataset containing 185 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Quercus peninsularis Trel."
 ]
}

 The dataset includes 185 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007245-181003121212138/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.h64cvh
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Uresiphita fusei Inoue, 1982",
 "HasCoordinate is true",
 "Country is Japan",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0210252-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hh5xc4
title: Occurrence Download

description: A dataset containing 119 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Platypodiella spectabilis (Herbst, 1794)"
 ]
}

 The dataset includes 119 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023235-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w7c2oc
title: Occurrence Download

description: A dataset containing 17 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Neacomys musseri Patton, da Silva & Malcolm, 2000",
 "HasCoordinate is TRUE"
 ]
}

 The dataset includes 17 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006387-180508205500799/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.74cpch
title: Occurrence Download

description: A dataset containing 12 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Iberis corifolia (Sims) Sweet"
 ]
}

 The dataset includes 12 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0152896-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3tsvc9
title: Occurrence Download

description: A dataset containing 45 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Orobanche lavandulacea Hohen. ex Reut., 1847"
 ]
}

 The dataset includes 45 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055066-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wl79hg
title: Occurrence Download

description: A dataset containing 789 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "Country is Spain",
 "Country is Portugal",
 "Country is Morocco",
 "Country is Algeria"
 ]
 },
 {
 "or" : [
 "Issue is Coordinate reprojected",
 "Issue is Coordinate rounded"
 ]
 },
 "TaxonKey is Pyrus bourgaeana Decne."
 ]
}

 The dataset includes 789 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007246-180131172636756/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5s2f3p
title: Occurrence Download

description: A dataset containing 29 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((-86.2850906000000037 32.3719475999999986, -86.2850906000000037 32.3735393899999977, -86.2850906000000037 32.3758386499999986, -86.2850906000000037 32.3777841799999990, -86.2850906000000037 32.3791991100000018, -86.2850906000000037 32.3807909000000009, -86.2827041500000007 32.3807909000000009, -86.2815551399999947 32.3807909000000009, -86.2794338900000071 32.3807909000000009, -86.2774894199999949 32.3807909000000009, -86.2759868699999970 32.3807909000000009, -86.2740423900000053 32.3807909000000009, -86.2727166100000034 32.3807909000000009, -86.2706837000000064 32.3807909000000009, -86.2706837000000064 32.3792875400000000, -86.2706837000000064 32.3782263399999977, -86.2706837000000064 32.3768114200000028, -86.2706837000000064 32.3753964900000000, -86.2706837000000064 32.3734509599999996, -86.2706837000000064 32.3719475999999986, -86.2730701499999952 32.3719475999999986, -86.2756333199999972 32.3719475999999986, -86.2775778000000031 32.3719475999999986, -86.2791687400000029 32.3719475999999986, -86.2804061300000029 32.3719475999999986, -86.2824389900000028 32.3719475999999986, -86.2834112299999987 32.3719475999999986, -86.2850906000000037 32.3719475999999986))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1932",
 "BasisOfRecord is one of (Human Observation, Living Specimen, Observation, Specimen, Machine Observation)"
 ]
}

 The dataset includes 29 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0097139-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6v56hg
title: Occurrence Download

description: A dataset containing 733 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation)",
 "CoordinateUncertaintyInMeters 0.0-31.0",
 "Country is United States of America",
 "Geometry POLYGON((-136.25583 22.09077,-53.80189 22.09077,-53.80189 52.37046,-136.25583 52.37046,-136.25583 22.09077))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Avena fatua L."
 ]
}

 The dataset includes 733 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023255-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.08kfa9
title: Occurrence Download

description: A dataset containing 1300 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Data from vegetation plots at Atiquipa, Southern Peru"
 ]
}

 The dataset includes 1300 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007246-181108115102211/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.