OGB Large-Scale Challenge (OGB-LSC) is a collection of three real-world datasets for advancing the state-of-the-art in large-scale graph ML. OGB-LSC provides graph datasets that are orders of magnitude larger than existing ones and covers three core graph learning tasks -- link prediction, graph regression, and node classification.

OGB-LSC consists of three datasets: MAG240M-LSC, WikiKG90M-LSC, and PCQM4M-LSC. Each dataset offers an independent task.

MAG240M-LSC is a heterogeneous academic graph, and the task is to predict the subject areas of papers situated in the heterogeneous graph (node classification). WikiKG90M-LSC is a knowledge graph, and the task is to impute missing triplets (link prediction). PCQM4M-LSC is a quantum chemistry dataset, and the task is to predict an important molecular property, the HOMO-LUMO gap, of a given molecule (graph regression).

The Open Graph Benchmark (OGB) is a collection of realistic, large-scale, and diverse benchmark datasets for machine learning on graphs. OGB datasets are automatically downloaded, processed, and split using the OGB Data Loader. The model performance can be evaluated using the OGB Evaluator in a unified manner. OGB is a community-driven initiative in active development.

'ogbg-molpcba' is a molecular dataset sampled from PubChem BioAssay. It is a graph prediction dataset from the Open Graph Benchmark (OGB).

This dataset is experimental, and the API is subject to change in future releases.

The below description of the dataset is adapted from the OGB paper:

Input Format

All the molecules are pre-processed using RDKit ([1]).

• Each graph represents a molecule, where nodes are atoms, and edges are chemical bonds.
• Input node features are 9-dimensional, containing atomic number and chirality, as well as other additional atom features such as formal charge and whether the atom is in the ring.
• Input edge features are 3-dimensional, containing bond type, bond stereochemistry, as well as an additional bond feature indicating whether the bond is conjugated.

The exact description of all features is available at https://github.com/snap-stanford/ogb/blob/master/ogb/utils/features.py.

Prediction

The task is to predict 128 different biological activities (inactive/active). See [2] and [3] for more description about these targets. Not all targets apply to each molecule: missing targets are indicated by NaNs.

References

[1]: Greg Landrum, et al. 'RDKit: Open-source cheminformatics'. URL: https://github.com/rdkit/rdkit

[2]: Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding and Vijay Pande. 'Massively Multitask Networks for Drug Discovery'. URL: https://arxiv.org/pdf/1502.02072.pdf

[3]: Zhenqin Wu, Bharath Ramsundar, Evan N Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S. Pappu, Karl Leswing, and Vijay Pande. MoleculeNet: a benchmark for molecular machine learning. Chemical Science, 9(2):513-530, 2018.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('ogbg_molpcba', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

https://storage.googleapis.com/tfds-data/visualization/fig/ogbg_molpcba-0.1.3.png" alt="Visualization" width="500px">

The Open Graph Benchmark (OGB) is a collection of realistic, large-scale, and diverse benchmark datasets for machine learning on graphs. OGB datasets are automatically downloaded, processed, and split using the OGB Data Loader. The model performance can be evaluated using the OGB Evaluator in a unified manner. OGB is a community-driven initiative in active development.

zkchen/OGB dataset hosted on Hugging Face and contributed by the HF Datasets community

OGBN-Proteins

Webpage: https://ogb.stanford.edu/docs/nodeprop/#ogbn-proteins

Usage in Python

import os.path as osp
import pandas as pd
import torch
import torch_geometric.transforms as T
from ogb.nodeproppred import PygNodePropPredDataset

class PygOgbnProteins(PygNodePropPredDataset):
  def _init_(self, meta_csv = None):
    root, name, transform = '/kaggle/input', 'ogbn-proteins', T.ToSparseTensor()
    if meta_csv is None:
      meta_csv = osp.join(root, name, 'ogbn-master.csv')
    master = pd.read_csv(meta_csv, index_col = 0)
    meta_dict = master[name]
    meta_dict['dir_path'] = osp.join(root, name)
    super()._init_(name = name, root = root, transform = transform, meta_dict = meta_dict)
  def get_idx_split(self, split_type = None):
    if split_type is None:
      split_type = self.meta_info['split']
    path = osp.join(self.root, 'split', split_type)
    if osp.isfile(os.path.join(path, 'split_dict.pt')):
      return torch.load(os.path.join(path, 'split_dict.pt'))
    if self.is_hetero:
      train_idx_dict, valid_idx_dict, test_idx_dict = read_nodesplitidx_split_hetero(path)
      for nodetype in train_idx_dict.keys():
        train_idx_dict[nodetype] = torch.from_numpy(train_idx_dict[nodetype]).to(torch.long)
        valid_idx_dict[nodetype] = torch.from_numpy(valid_idx_dict[nodetype]).to(torch.long)
        test_idx_dict[nodetype] = torch.from_numpy(test_idx_dict[nodetype]).to(torch.long)
        return {'train': train_idx_dict, 'valid': valid_idx_dict, 'test': test_idx_dict}
    else:
      train_idx = dt.fread(osp.join(path, 'train.csv'), header = None).to_numpy().T[0]
      train_idx = torch.from_numpy(train_idx).to(torch.long)
      valid_idx = dt.fread(osp.join(path, 'valid.csv'), header = None).to_numpy().T[0]
      valid_idx = torch.from_numpy(valid_idx).to(torch.long)
      test_idx = dt.fread(osp.join(path, 'test.csv'), header = None).to_numpy().T[0]
      test_idx = torch.from_numpy(test_idx).to(torch.long)
      return {'train': train_idx, 'valid': valid_idx, 'test': test_idx}

dataset = PygOgbnProteins()
split_idx = dataset.get_idx_split()
train_idx, valid_idx, test_idx = split_idx['train'], split_idx['valid'], split_idx['test']
graph = dataset[0] # PyG Graph object

Description

Graph: The ogbn-proteins dataset is an undirected, weighted, and typed (according to species) graph. Nodes represent proteins, and edges indicate different types of biologically meaningful associations between proteins, e.g., physical interactions, co-expression or homology [1,2]. All edges come with 8-dimensional features, where each dimension represents the strength of a single association type and takes values between 0 and 1 (the larger the value is, the stronger the association is). The proteins come from 8 species.

Prediction task: The task is to predict the presence of protein functions in a multi-label binary classification setup, where there are 112 kinds of labels to predict in total. The performance is measured by the average of ROC-AUC scores across the 112 tasks.

Dataset splitting: The authors split the protein nodes into training/validation/test sets according to the species which the proteins come from. This enables the evaluation of the generalization performance of the model across different species.

Note: For undirected graphs, the loaded graphs will have the doubled number of edges because the bidirectional edges will be added automatically.

Summary

Open Graph Benchmark

Website: https://ogb.stanford.edu

The Open Graph Benchmark (OGB) [3] is a collection of realistic, large-scale, and diverse benchmark datasets for machine learning on graphs. OGB datasets are automatically downloaded, processed, and split using the OGB Data Loader. The model performance can be evaluated using the OGB Evaluator in a unified manner.

References

[1] Damian Szklarczyk, Annika L Gable, David Lyon, Alexander Junge, Stefan Wyder, Jaime Huerta-Cepas, Milan Simonovic, Nadezhda T Doncheva, John H Morris, Peer Bork, et al. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Research, 47(D1):D607–D613, 2019. [2] Gene Ontology Consortium. The gene ontology resource: 20 years and still going strong. Nucleic Acids Research, 47(D1):D330–D338, 2018. [3] Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. Open graph benchmark: Datasets for machine learning on graphs. Advances in Neural Information Processing Systems, pp. 22118–22133, 2020.

Disclaimer

I am NOT the author of this dataset. It was downloaded from its official website. I assume no responsibility or liability for the content in this dataset. Any questions, problems or issues, please contact the original authors at their website or their GitHub repo.

CORA, Citeseer, Pubmed, OGB arXiv

OGB-LSC provides the three large-scale realistic benchmark datasets, covering the core graph ML tasks of node classification, link prediction, and graph regression.

Datasets and splits of the manuscript "Chemprop: Machine Learning Package for Chemical Property Prediction." Train, validation and test splits are located within each folder, as well as additional data necessary for some of the benchmarks. To train Chemprop models, refer to our code repository to obtain ready-to-use scripts to train machine learning models for each of the systems. Available benchmarking systems:

hiv HIV replication inhibition from MoleculeNet and OGB with scaffold splits pcba_random Biological activities from MoleculeNet with random splits (with missing targets filled in with zeros as provided by MoleculeNet) pcba_random_nans Biological activities from MoleculeNet with random splits and data format to match OGB (with missing targets not filled in with zeros) pcba_scaffold Biological activities from OGB with scaffold splits qm9_multitask DFT calculated properties from MoleculeNet and OGB, trained as a multi-task model qm9_u0 DFT calculated properties from MoleculeNet and OGB, trained as a single-task model on the target U0 only qm9_gap DFT calculated properties from MoleculeNet and OGB, trained as a single-task model on the target gap only sampl Water-octanol partition coefficients, used to predict molecules from the SAMPL6, 7 and 9 challenges atom_bond_137k Quantum-mechanical atom and bond descriptors bde Bond dissociation enthalpies trained as single-task model bde_charges Bond dissociation enthalpies trained as multi-task model together with atomic partial charges charges_eps_4 Partial charges at a dielectric constant of 4 (in protein) charges_eps_78 Partial charges at a dielectric constant of 78 (in water) barriers_e2 Reaction barrier heights of E2 reactions barriers_sn2 Reaction barrier heights of SN2 reactions barriers_cycloadd Reaction barrier heights of cycloaddition reactions barriers_rdb7 Reaction barrier heights in the RDB7 dataset barriers_rgd1 Reaction barrier heights in the RGD1-CNHO dataset multi_molecule UV/Vis peak absorption wavelengths in different solvents ir IR Spectra pcqm4mv2 HOMO-LUMO gaps of the PCQM4Mv2 dataset uncertainty_ensemble Uncertainty estimation using an ensemble using the QM9 gap dataset uncertainty_evidential Uncertainty estimation using evidential learning using the QM9 gap dataset uncertainty_mve Uncertainty estimation using mean-variance estimation using the QM9 gap dataset timing Timing benchmark using subsets of QM9 gap Version: This version of the dataset (Version 2) is compatible with all versions of Chemprop (supporting the respective functionality). Version 1 of this dataset is compatible with all versions except Chemprop v.1.6.1, which cannot process the charges_eps_4 and charges_eps_78 datasets (all other benchmarks work as expected). We therefore recommend to always use Version 2 of the dataset (with reformatted charges_eps_4 and charges_eps_78 datasets), since it is compatible with all versions of Chemprop. For use with any other ML software, you can use any version.

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Investigate historical ownership changes and registration details by initiating a reverse Whois lookup for the name Vintage Ogb.

Used TheBloke/OpenHermes-2-Mistral-7B-GPTQ to convert chunks into QA pairs used for finetuning

Calcium time series from OGB labeled V1 neurons in awake or anesthesized mice. Data published in: Pieter M. Goltstein, Jorrit S. Montijn, Cyriel M.A. Pennartz. (2015). Effects of isoflurane anesthesia on ensemble patterns of Ca2+ activity in mouse V1: Reduced direction selectivity independent of increased correlations in cellular activity. PLOS ONE.

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ogbrandt/pjf_llama_instruction_prep dataset hosted on Hugging Face and contributed by the HF Datasets community

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

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ogbrandt/nous-pjf dataset hosted on Hugging Face and contributed by the HF Datasets community

Specifications of Dataset Download in Geom3D

We provide both the raw and processed data at this HuggingFace link.

  PCQM4Mv2

mkdir -p pcqm4mv2/raw cd pcqm4mv2/raw wget http://ogb-data.stanford.edu/data/lsc/pcqm4m-v2-train.sdf.tar.gz tar -xf pcqm4m-v2-train.sdf.tar.gz

wget http://ogb-data.stanford.edu/data/lsc/pcqm4m-v2.zip unzip pcqm4m-v2.zip mv pcqm4m-v2/raw/data.csv.gz . rm pcqm4m-v2.zip rm -rf pcqm4m-v2

  GEOM

wget… See the full description on the dataset page: https://huggingface.co/datasets/chao1224/Geom3D_data.

ogbrandt/gpt4_preference_rlaif dataset hosted on Hugging Face and contributed by the HF Datasets community