cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.afcsr5
title: Occurrence Download

description: A dataset containing 15 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is Pakistan",
 "GadmGid is PAK.5_1",
 "TaxonKey is Ailanthus altissima"
 ]
}

 The dataset includes 15 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022907-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17102662
title: GC3 Analysis of Global SARS-CoV-2 Genomic Composition Across 35.3 Million Sequences

description: Dataset of GC-content at third codon positions (gc3_ratio) for 35,376,500 SARS-CoV-2 genome sequences.

Each record includes:-    header   : Unique sample identifier (UUID format, traceable to source)-    gc3_ratio   : GC content ratio at 3rd codon position - critical for studying codon usage bias, mRNA stability, and evolutionary pressure.

Summary Statistics:- Total records: 35,376,500- Unique samples: 35,376,500- Mean GC3 Ratio: 0.392- Std Dev: 0.041- Range: 0.298 - 0.435- Files merged: 3,540- Corrupted files skipped: 1

For furtherr analysis on:- Evolutionary genomics of SARS-CoV-2- mRNA vaccine design optimization- Machine learning on codon usage features- Quality control in viral sequencing pipelines

LICENSE: CC-BY-4.0 (Please cite my work if data is used!)GVAtlas.Org
DOI: 10.5281/zenodo.17102661
title: GC3 Analysis of Global SARS-CoV-2 Genomic Composition Across 35.3 Million Sequences

description: Dataset of GC-content at third codon positions (gc3_ratio) for 35,376,500 SARS-CoV-2 genome sequences.

Each record includes:-    header   : Unique sample identifier (UUID format, traceable to source)-    gc3_ratio   : GC content ratio at 3rd codon position - critical for studying codon usage bias, mRNA stability, and evolutionary pressure.

Summary Statistics:- Total records: 35,376,500- Unique samples: 35,376,500- Mean GC3 Ratio: 0.392- Std Dev: 0.041- Range: 0.298 - 0.435- Files merged: 3,540- Corrupted files skipped: 1

For furtherr analysis on:- Evolutionary genomics of SARS-CoV-2- mRNA vaccine design optimization- Machine learning on codon usage features- Quality control in viral sequencing pipelines

LICENSE: CC-BY-4.0 (Please cite my work if data is used!)GVAtlas.Org
DOI: 10.15468/dl.jm3vm5
title: Occurrence Download

description: A dataset containing 36434 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Phalacrocorax carbo",
 "Country is Switzerland",
 "HasCoordinate is true"
 ]
}

 The dataset includes 36434 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022905-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17100784
title: PR2 Analysis of Global SARS-CoV-2 Genomic Composition Across 35.3 Million Sequences

description: Comprehensive dataset of nucleotide composition metrics and classification scores for 35,376,500 SARS-CoV-2 genome sequences, processed through the PR2 pipeline.

Each record includes:- Sample identifier (header) with embedded geographic, lab, and temporal metadata (e.g., "USA/CA-LACPHL-AF03923/2021")- Nucleotide counts at third codon positions (a3s, t3s, g3s, c3s) - critical for studying mutational bias, evolutionary pressure, and QC filtering- PR2 classification confidence score (pr2_score) - for filtering high-confidence sequences

Summary Statistics:- Total records: 35,375,835- Unique samples: 9,355,333- Year range: 2019 to 2025- Top country: USA (14,493,148 records)- Mean PR2 Score: 0.698 - indicating overall high classification confidence

For analysis of:- Large-scale evolutionary analysis of SARS-CoV-2- Geographic and temporal trend mapping- Machine learning on genomic composition features- Quality control benchmarking in viral genomics

Data files available on request!

Open Science License: CC-BY-4.0 - please cite if used.
DOI: 10.5281/zenodo.17100785
title: PR2 Analysis of Global SARS-CoV-2 Genomic Composition Across 35.3 Million Sequences

description: Comprehensive dataset of nucleotide composition metrics and classification scores for 35,376,500 SARS-CoV-2 genome sequences, processed through the PR2 pipeline.

Each record includes:- Sample identifier (header) with embedded geographic, lab, and temporal metadata (e.g., "USA/CA-LACPHL-AF03923/2021")- Nucleotide counts at third codon positions (a3s, t3s, g3s, c3s) - critical for studying mutational bias, evolutionary pressure, and QC filtering- PR2 classification confidence score (pr2_score) - for filtering high-confidence sequences

Summary Statistics:- Total records: 35,375,835- Unique samples: 9,355,333- Year range: 2019 to 2025- Top country: USA (14,493,148 records)- Mean PR2 Score: 0.698 - indicating overall high classification confidence

For analysis of:- Large-scale evolutionary analysis of SARS-CoV-2- Geographic and temporal trend mapping- Machine learning on genomic composition features- Quality control benchmarking in viral genomics

Data files available on request!

Open Science License: CC-BY-4.0 - please cite if used.
DOI: 10.6084/m9.figshare.30363622.v1
title: Distinct Microbial Life History Strategies Across Soil Depth Profiles: Metagenome Analysis of a Long-Term Temperate Agricultural Field Experiment

description: Soil Properties Data
DOI: 10.6084/m9.figshare.30363622
title: Distinct Microbial Life History Strategies Across Soil Depth Profiles: Metagenome Analysis of a Long-Term Temperate Agricultural Field Experiment

description: Soil Properties Data
DOI: 10.6084/m9.figshare.30363619
title: Table: Detoxification Related Genes in PR5 Strain

description: Table: Detoxification Related Genes in PR5 Strain
DOI: 10.6084/m9.figshare.30363619.v1
title: Table: Detoxification Related Genes in PR5 Strain

description: Table: Detoxification Related Genes in PR5 Strain
DOI: 10.6084/m9.figshare.30362707
title: Th genome data used in S. aureus CC59 analysis

description: A total of 3994 genome data were used in the analysis of "<b>Global emergence and evolution of </b><b><i>Staphylococcus aureus</i></b><b> clonal complex 59</b>"
DOI: 10.6084/m9.figshare.30362707.v1
title: Th genome data used in S. aureus CC59 analysis

description: A total of 3994 genome data were used in the analysis of "<b>Global emergence and evolution of </b><b><i>Staphylococcus aureus</i></b><b> clonal complex 59</b>"
DOI: 10.5281/zenodo.17308407
title: Synthetic Pallet Datasets for Fine-Tuning Faster R-CNN (Realistic / Half-Realistic / Random)

description: Synthetic Pallet Datasets for Fine-Tuning Faster R-CNN

This archive provides three synthetic datasets used to study the impact of realism and domain randomization on pallet detection when fine-tuning a pre-trained Faster R-CNN. Each dataset corresponds to a single generation recipe – realistic, half-realistic, or random – and is packaged as a separate ZIP file.

ZIP files: realistic.zip, halfrealistic.zip, random.zipInside each ZIP: four splits





test/




train_10_percent/




train_50_percent/




train_100_percent/



Each split contains:





annotations/ – ground-truth bounding box files (one file per image).




images/ – RGB images used for training/evaluation.




(train_ only)* Best model weights per seed as separate .pth files.




(train_ only)* Training logs and the corresponding in-distribution (ID) and out-of-distribution (OOD) test performance after fine-tuning on that split.




(train_ only)* Per-seed performance visualizations.



Intended Use. Reproduce our experiments, compare training strategies (mixed vs. bridged transfer), or benchmark alternative detectors in low-data settings.

Source / Creation Process. Images were generated with NVIDIA Omniverse Replicator using three variant recipes (Realistic / Half-Realistic / Random). Ground-truth boxes were produced during rendering; evaluation splits mirror those in the paper.File Organization (Example)random.zip ├─ test/ │   ├─ annotations/ │   └─ images/ ├─ train_10_percent/ │   ├─ annotations/ │   ├─ images/ │   ├─ *_ood_test_log_seed_*.txt │   ├─ *_test_log_seed_*.txt │   ├─ *_training_log_seed_*.txt │   ├─ *.png per seed │   └─ best *.pth per seed ├─ train_50_percent/ (same structure) └─ train_100_percent/ (same structure)

Related Resources

Project Page: https://muammerbay.github.io/omniverse-replicator-sim2real-analysis/ GitHub: https://github.com/MuammerBay/omniverse-replicator-sim2real-analysisarXiv: https://arxiv.org/abs/2510.12208

How to Cite

@misc{bay2025impactsyntheticdataobject,      title={The Impact of Synthetic Data on Object Detection Model Performance: A Comparative Analysis with Real-World Data},       author={Muammer Bay and Timo von Marcard and Dren Fazlija},      year={2025},      eprint={2510.12208},      archivePrefix={arXiv},      primaryClass={cs.CV},      url={https://arxiv.org/abs/2510.12208}, }
DOI: 10.5281/zenodo.17308406
title: Synthetic Pallet Datasets for Fine-Tuning Faster R-CNN (Realistic / Half-Realistic / Random)

description: Synthetic Pallet Datasets for Fine-Tuning Faster R-CNN

This archive provides three synthetic datasets used to study the impact of realism and domain randomization on pallet detection when fine-tuning a pre-trained Faster R-CNN. Each dataset corresponds to a single generation recipe – realistic, half-realistic, or random – and is packaged as a separate ZIP file.

ZIP files: realistic.zip, halfrealistic.zip, random.zipInside each ZIP: four splits





test/




train_10_percent/




train_50_percent/




train_100_percent/



Each split contains:





annotations/ – ground-truth bounding box files (one file per image).




images/ – RGB images used for training/evaluation.




(train_ only)* Best model weights per seed as separate .pth files.




(train_ only)* Training logs and the corresponding in-distribution (ID) and out-of-distribution (OOD) test performance after fine-tuning on that split.




(train_ only)* Per-seed performance visualizations.



Intended Use. Reproduce our experiments, compare training strategies (mixed vs. bridged transfer), or benchmark alternative detectors in low-data settings.

Source / Creation Process. Images were generated with NVIDIA Omniverse Replicator using three variant recipes (Realistic / Half-Realistic / Random). Ground-truth boxes were produced during rendering; evaluation splits mirror those in the paper.File Organization (Example)random.zip ├─ test/ │   ├─ annotations/ │   └─ images/ ├─ train_10_percent/ │   ├─ annotations/ │   ├─ images/ │   ├─ *_ood_test_log_seed_*.txt │   ├─ *_test_log_seed_*.txt │   ├─ *_training_log_seed_*.txt │   ├─ *.png per seed │   └─ best *.pth per seed ├─ train_50_percent/ (same structure) └─ train_100_percent/ (same structure)

Related Resources

Project Page: https://muammerbay.github.io/omniverse-replicator-sim2real-analysis/ GitHub: https://github.com/MuammerBay/omniverse-replicator-sim2real-analysisarXiv: https://arxiv.org/abs/2510.12208

How to Cite

@misc{bay2025impactsyntheticdataobject,      title={The Impact of Synthetic Data on Object Detection Model Performance: A Comparative Analysis with Real-World Data},       author={Muammer Bay and Timo von Marcard and Dren Fazlija},      year={2025},      eprint={2510.12208},      archivePrefix={arXiv},      primaryClass={cs.CV},      url={https://arxiv.org/abs/2510.12208}, }
DOI: 10.15468/dl.uhfh8v
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Strigiformes)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Geometry POLYGON((160 10,170 10,170 20,160 20,160 10))"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022909-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17095547
title: Gene-resolved CpG and UpA dinucleotide indices across 4.9 million SARS-CoV-2 genomes

description: This dataset contains gene-specific CpG and UpA dinucleotide indices calculated across 4,914,000 SARS-CoV-2 genomes.

Indices are provided for 13 open reading frames: ORF1a, ORF1b, S, E, M, N, ORF3a, ORF6, ORF7a, ORF7b, ORF8, ORF9b, and ORF10.

For each gene, the dataset includes:

Mean, median, standard deviation, min/max, and quartiles for CpG and UpA indicesWilcoxon signed-rank test results comparing CpG vs UpA within each gene (sample size = 100,000)Data is provided in machine-readable CSV format and individual gene reports in .txt

Note: Indices for short genes (e.g., E, ORF10) may exceed 1.0 due to normalization methods and should be interpreted with caution.

 

======================================================================SARS-CoV-2 GENE-RESOLVED CpG AND UpA INDICES - SUMMARYDataset: 4,914,000 genomesGenes: ORF1a, ORF1b, S, E, M, N, ORF3a, ORF6, ORF7a, ORF7b, ORF8, ORF9b, ORF10Method: Wilcoxon signed-rank test (sample n=100,000 per gene)======================================================================

Gene     CpG_mean UpG_mean Δ(UpG-CpG)   Wilcoxon_p  Sig----------------------------------------------------------------------ORF1a      0.3433   0.8485     0.5052      <1e-100  YESORF1b      0.4161   0.8904     0.4743      <1e-100  YESS          0.2333   0.7117     0.4784      <1e-100  YESE          1.3435   1.0813    -0.2622      <1e-100  YESM          0.6568   0.8491     0.1923      <1e-100  YESN          0.5664   0.5128    -0.0537      <1e-100  YESORF3a      0.5334   0.7215     0.1881      <1e-100  YESORF6       0.3785   0.8346     0.4561      <1e-100  YESORF7a      0.5192   0.6428     0.1236      <1e-100  YESORF7b      0.3296   0.6989     0.3693      <1e-100  YESORF8       0.6957   0.8972     0.2015      <1e-100  YESORF9b      0.8740   0.6780    -0.1961      <1e-100  YESORF10      1.4480   1.2525    -0.1955      <1e-100  YES

Note: Indices for short genes (E, ORF10) >1.0 may reflect normalization artifacts.Data format: CSV + individual gene reports (.txt). Calculated in Python (Pandas/SciPy).

GVAtlas.Org
DOI: 10.5281/zenodo.17095546
title: Gene-resolved CpG and UpA dinucleotide indices across 4.9 million SARS-CoV-2 genomes

description: This dataset contains gene-specific CpG and UpA dinucleotide indices calculated across 4,914,000 SARS-CoV-2 genomes.

Indices are provided for 13 open reading frames: ORF1a, ORF1b, S, E, M, N, ORF3a, ORF6, ORF7a, ORF7b, ORF8, ORF9b, and ORF10.

For each gene, the dataset includes:

Mean, median, standard deviation, min/max, and quartiles for CpG and UpA indicesWilcoxon signed-rank test results comparing CpG vs UpA within each gene (sample size = 100,000)Data is provided in machine-readable CSV format and individual gene reports in .txt

Note: Indices for short genes (e.g., E, ORF10) may exceed 1.0 due to normalization methods and should be interpreted with caution.

 

======================================================================SARS-CoV-2 GENE-RESOLVED CpG AND UpA INDICES - SUMMARYDataset: 4,914,000 genomesGenes: ORF1a, ORF1b, S, E, M, N, ORF3a, ORF6, ORF7a, ORF7b, ORF8, ORF9b, ORF10Method: Wilcoxon signed-rank test (sample n=100,000 per gene)======================================================================

Gene     CpG_mean UpG_mean Δ(UpG-CpG)   Wilcoxon_p  Sig----------------------------------------------------------------------ORF1a      0.3433   0.8485     0.5052      <1e-100  YESORF1b      0.4161   0.8904     0.4743      <1e-100  YESS          0.2333   0.7117     0.4784      <1e-100  YESE          1.3435   1.0813    -0.2622      <1e-100  YESM          0.6568   0.8491     0.1923      <1e-100  YESN          0.5664   0.5128    -0.0537      <1e-100  YESORF3a      0.5334   0.7215     0.1881      <1e-100  YESORF6       0.3785   0.8346     0.4561      <1e-100  YESORF7a      0.5192   0.6428     0.1236      <1e-100  YESORF7b      0.3296   0.6989     0.3693      <1e-100  YESORF8       0.6957   0.8972     0.2015      <1e-100  YESORF9b      0.8740   0.6780    -0.1961      <1e-100  YESORF10      1.4480   1.2525    -0.1955      <1e-100  YES

Note: Indices for short genes (E, ORF10) >1.0 may reflect normalization artifacts.Data format: CSV + individual gene reports (.txt). Calculated in Python (Pandas/SciPy).

GVAtlas.Org
DOI: 10.15468/dl.559djg
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Strigiformes)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Geometry POLYGON((150 10,160 10,160 20,150 20,150 10))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022911-251009101135966/datasets/export for details.
DOI: 10.15468/dl.bkpr2r
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Phaethontiformes)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Geometry POLYGON((150 10,160 10,160 20,150 20,150 10))"
 ]
}

 The dataset includes 4 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022910-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17052827
title: CPB Scores of SARS-CoV-2 Genomes (n = 18,390,482)

description: Dataset: CPB_SCORESSource: cpb_scores_results.zipDescription: Codon Pair Bias (CPB) scores for SARS-CoV-2 genomes.

Total TSV files: 1Sample file: cpb_scores.tsvTotal genomic records: 18,390,482Columns: strain,date,lineage,accession,S_mutations,E_mutations,M_mutations,N_mutations,S_cpb,E_cpb,M_cpb,N_cpb,total_mutationsData Preview:  Row 1: unknown,unknown,Other,USA/CA-LACPHL-A,,,,,0.600658 ...  Row 2: unknown,unknown,Other,USA/CA-CDC-FG-1,S:654Y>N;S:1 ...  Row 3: unknown,unknown,Other,USA/PA-BOL-M211,S:298K>syn;S ...

--------------------------------------------------------------------------

Researcher: TahirHB@hotmail.com

 
description: Note: Duplicate or near-identical entries are retained to preserve temporal resolution and analytical robustness across the pandemic timeline.
DOI: 10.15468/dl.v5wuzc
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Salariopsis fluviatilis, Gnathochromis pfefferi, Chalinochromis brichardi, Chalinochromis cyanophleps, Chalinochromis popelini)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "Issue" : [
 "is one of (Recorded date invalid, Recorded date mismatch, Recorded date unlikely, Modified date invalid)"
 ]
 }
 },
 "Geometry MULTIPOLYGON (((39.5387 17.0726, 39.1348 18.0388, 39.3493 18.6268, 39.116 19.1892, 38.3245 19.7041, 37.7662 19.7333, 37.7663 20.0599, 37.7668 21.2899, 37.3972 21.7772, 37.3005 22.3477, 37.1382 22.4869, 37.1382 20.0599, 37.1382 11.0599, 37.1382 2.0599, 37.1382 -6.9401, 40.3593 -6.9401, 40.412 -6.7736, 40.0778 -6.426, 39.9615 -5.9146, 40.3471 -5.2827, 40.3421 -4.7259, 40.0631 -4.4307, 40.3789 -3.7221, 40.7318 -3.0429, 41.8321 -2.1586, 43.8394 0.2906, 44.9094 1.1982, 46.1382 1.9309, 46.1382 2.0599, 46.1382 11.0599, 46.1382 11.2722, 45.66 11.3473, 45.0861 11.0599, 44.7982 10.9157, 44.5146 10.8961, 44.3186 11.0599, 44.0798 11.2595, 43.8495 11.8222, 43.8305 12.54, 43.3858 13.2657, 41.2508 15.1278, 41.3523 15.6631, 41.1413 16.086, 40.7405 16.3725, 40.5343 16.9022, 39.5387 17.0726)), ((40.3849 -21.8351, 39.9523 -22.0203, 39.831 -22.3517, 39.978 -22.7301, 40.3629 -22.8988, 40.7455 -22.7347, 40.8996 -22.3696, 40.751 -22.0066, 40.3849 -21.8351)), ((43.501 -16.1338, 43.501 -16.5181, 43.7601 -16.7795, 43.2797 -17.3607, 43.4853 -17.9002, 43.2454 -18.319, 43.3669 -18.7475, 43.7392 -19.0523, 43.9101 -19.9313, 43.3088 -20.6095, 42.6924 -22.1145, 43.1438 -23.4412, 43.2023 -24.5126, 43.4152 -24.9401, 46.1382 -24.9401, 46.1382 -15.9401, 46.1382 -15.1614, 46.0559 -15.2167, 45.4781 -15.3105, 44.725 -15.7042, 43.7407 -15.8768, 43.6816 -15.9401, 43.501 -16.1338)), ((42.4892 -17.5028, 42.907 -17.5351, 43.1823 -17.3111, 43.2262 -16.9066, 42.991 -16.6199, 42.5693 -16.563, 42.237 -16.8562, 42.2373 -17.2371, 42.4892 -17.5028)), ((39.6106 -17.1715, 40.0468 -17.0272, 40.4338 -16.6515, 40.833 -15.9401, 37.1382 -15.9401, 37.1382 -18.642, 37.5549 -18.1303, 38.5882 -17.6405, 39.1071 -17.6835, 39.6106 -17.1715)))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022914-251009101135966/datasets/export for details.
DOI: 10.5281/zenodo.17052828
title: CPB Scores of SARS-CoV-2 Genomes (n = 18,390,482)

description: Dataset: CPB_SCORESSource: cpb_scores_results.zipDescription: Codon Pair Bias (CPB) scores for SARS-CoV-2 genomes.

Total TSV files: 1Sample file: cpb_scores.tsvTotal genomic records: 18,390,482Columns: strain,date,lineage,accession,S_mutations,E_mutations,M_mutations,N_mutations,S_cpb,E_cpb,M_cpb,N_cpb,total_mutationsData Preview:  Row 1: unknown,unknown,Other,USA/CA-LACPHL-A,,,,,0.600658 ...  Row 2: unknown,unknown,Other,USA/CA-CDC-FG-1,S:654Y>N;S:1 ...  Row 3: unknown,unknown,Other,USA/PA-BOL-M211,S:298K>syn;S ...

--------------------------------------------------------------------------

Researcher: TahirHB@hotmail.com

 
description: Note: Duplicate or near-identical entries are retained to preserve temporal resolution and analytical robustness across the pandemic timeline.
DOI: 10.5281/zenodo.17052490
title: SARS-CoV-2 Translational Speed Across the Pandemic (n = 9,356,279)

description: Dataset: TRANSLATIONAL_SPEEDSource: translational_speed.zipDescription: Translational speed predictions across SARS-CoV-2 genomes.

Total TSV files: 1Sample file: translational_speed.tsvTotal genomic records: 9,356,279Columns: strain,date,lineage,accession,S_mutations,E_mutations,M_mutations,N_mutations,S_speed,E_speed,M_speed,N_speed,total_mutationsData Preview:  Row 1: unknown,unknown,Other,USA/CA-LACPHL-A,,,,,0.651493 ...  Row 2: unknown,unknown,Other,USA/CA-CDC-FG-1,S:654Y>N;S:1 ...  Row 3: unknown,unknown,Other,USA/PA-BOL-M211,S:298K>syn;S ...

----------------------------------------------------------------

Data generated by Tahir, H. B. (tahirhb@hotmail.com )
DOI: 10.15468/dl.tsttzd
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "AssociatedSequences is not null",
 {
 "or" : [
 "CatalogNumber is one of (MF100822, TU117096)",
 "RecordNumber is one of (MF100822, TU117096)"
 ]
 }
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022912-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.