Data collected from interviews and group discussions, called qualitative data, can help researchers understand people's experiences, values, and cultures. But large amounts of qualitative data can be hard to show in a way that's easy for people to understand. In this study, the research team created charts called ethnoarrays. These charts use color coding to show individual stories and overall patterns in qualitative data. The team wanted to learn whether ethnoarrays were useful and easy to understand.

Summary of network visualisation tools commonly used for the analysis of biological data.

This dataset provides a five-level, fine-grained, and structurally normalized knowledge-graph representation of a qualitative methods text corpus (Research with Qualitative Data), treated purely as text data rather than as a bibliographic object. Each record corresponds to a node at one of five hierarchical levels—macro-section (level 1), meso-section (level 2), paragraph (level 3), sentence (level 4), and keyword/media snippet (level 5)—with explicit parent–child links (e.g., sentence → paragraph, paragraph → meso-section), forming a well-closed, acyclic tree structure. For all machine-readable content in the source PDF, the dataset decomposes the corpus into independent nodes while preserving page locators and section titles, so that any fragment of text can be traced back to its exact position in the original file. Keyword nodes are automatically extracted from sentences to enhance search, thematic mapping, and downstream modeling without altering or compressing the underlying text. For tables and images, the dataset stores captions, surrounding textual context, and row-level data_points where applicable, enabling full reconstruction of tabular and visual information at the text level. Under the assumption that “all machine-readable text in the PDF is the reference universe,” the collection achieves a practically lossless representation of the qualitative methods corpus and has been independently checked for level completeness, parent–child consistency, and content integrity, supporting its designation as a five-level, completely lossless text-based knowledge-graph dataset suitable for advanced qualitative methodology research, knowledge-graph engineering, and large-language-model retrieval and reasoning experiments.

• Semi-structured interview guideline
• 7 anonomized Interview datasets with nine bioeconomy hubs of the RuralBioUp project
• transcript videos of RuralBioUp hubs
• graph on materials used for qualitative analysis

This dataset comprises a five-level, fine-grained, lossless knowledge graph (Version 2) constructed around full-text papers on advanced qualitative research and mixed-methods research methodologies. The source texts are complete, lengthy academic works covering the philosophical foundations of qualitative research, research design, specific methodological operations, and diverse case studies and mixed-methods practices. This dataset no longer preserves the original formatting and layout details. Instead, it systematically transforms the knowledge content into structured data organized as “whole-chapter-paragraph-sentence-keyword/heterogeneous node,” supporting methodological meta-research, instructional design, knowledge graph and GraphRAG modeling, as well as the development of intelligent retrieval and reasoning systems for academic texts. Version 2 significantly enhances paragraph-level representation, chart data preservation, and metadata annotation capabilities over its predecessor, balancing readability, computability, and methodological rigor.

Quantitative data underlying graphs published in Figs 1–5.

WIDEa is R-based software aiming to provide users with a range of functionalities to explore, manage, clean and analyse "big" environmental and (in/ex situ) experimental data. These functionalities are the following, 1. Loading/reading different data types: basic (called normal), temporal, infrared spectra of mid/near region (called IR) with frequency (wavenumber) used as unit (in cm-1); 2. Interactive data visualization from a multitude of graph representations: 2D/3D scatter-plot, box-plot, hist-plot, bar-plot, correlation matrix; 3. Manipulation of variables: concatenation of qualitative variables, transformation of quantitative variables by generic functions in R; 4. Application of mathematical/statistical methods; 5. Creation/management of data (named flag data) considered as atypical; 6. Study of normal distribution model results for different strategies: calibration (checking assumptions on residuals), validation (comparison between measured and fitted values). The model form can be more or less complex: mixed effects, main/interaction effects, weighted residuals.

Network of 43 papers and 65 citation links related to "Social media as a data gathering tool for international business qualitative research: opportunities and challenges".

The "Dataset_Graph.zip" file contains the graph models of the trees in the dataset. These graph models are saved in the "pickle" format, which is a binary format used for serializing Python objects. The graph models capture the structural information and relationships of the cylinders in each tree, representing the hierarchical organization of the branches.

Dataset containing supplemental material for the publication "2D, 2.5D, or 3D? An Exploratory Study on Multilayer Network Visualizations in Virtual Reality" This dataset contains: 1) archive containing all raw quantitative results, 2) archive containing all raw qualitative data, 3) archive containing the graphs used for the experiment (.graphml file format), 4) the code to generate the graph library (C++ files using OGDF), 5) a PDF document containing detailed results (with p-values and more charts), 6) a video showing the experimentation from a participant's point of view. 7) complete graph library generated by our graph generator for the experiment

OGBN-MolClinTox

Webpage: https://ogb.stanford.edu/docs/graphprop/#ogbg-mol

Usage in Python

import os
import os.path as osp
import pandas as pd
import torch
from ogb.graphproppred import PygGraphPropPredDataset

class PygOgbgMol(PygGraphPropPredDataset):
  def _init_(self, name, transform = None, pre_transform = None, meta_csv = None):
    root = '../input'
    if meta_csv is None:
      meta_csv = osp.join(root, name, 'ogbg-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, pre_transform = pre_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)

    # short-cut if split_dict.pt exists
    if os.path.isfile(os.path.join(path, 'split_dict.pt')):
      return torch.load(os.path.join(path, 'split_dict.pt'))

    train_idx = pd.read_csv(osp.join(path, 'train.csv'), header = None).values.T[0]
    valid_idx = pd.read_csv(osp.join(path, 'valid.csv'), header = None).values.T[0]
    test_idx = pd.read_csv(osp.join(path, 'test.csv'), header = None).values.T[0]

    return {'train': torch.tensor(train_idx, dtype = torch.long), 'valid': torch.tensor(valid_idx, dtype = torch.long), 'test': torch.tensor(test_idx, dtype = torch.long)}

dataset = PygOgbgMol('ogbg-molclintox')

from torch_geometric.data import DataLoader

batch_size = 32
split_idx = dataset.get_idx_split()
train_loader = DataLoader(dataset[split_idx['train']], batch_size = batch_size, shuffle = True)
valid_loader = DataLoader(dataset[split_idx['valid']], batch_size = batch_size, shuffle = False)
test_loader = DataLoader(dataset[split_idx['test']], batch_size = batch_size, shuffle = False)

Description

Graph: The ogbg-molhiv and ogbg-molpcba datasets are two molecular property prediction datasets of different sizes: ogbg-molhiv (small) and ogbg-molpcba (medium). They are adopted from the MoleculeNet [1], and are among the largest of the MoleculeNet datasets. All the molecules are pre-processed using RDKit [2]. 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 or not. The full description of the features is provided in code. The script to convert the SMILES string [3] to the above graph object can be found here. Note that the script requires RDKit to be installed. The script can be used to pre-process external molecule datasets so that those datasets share the same input feature space as the OGB molecule datasets. This is particularly useful for pre-training graph models, which has great potential to significantly increase generalization performance on the (downstream) OGB datasets [4].

Beside the two main datasets, the dataset authors additionally provide 10 smaller datasets from MoleculeNet. They are ogbg-moltox21, ogbg-molbace, ogbg-molbbbp, ogbg-molclintox, ogbg-molmuv, ogbg-molsider, and ogbg-moltoxcast for (multi-task) binary classification, and ogbg-molesol, ogbg-molfreesolv, and ogbg-mollipo for regression. Evaluators are also provided for these datasets. These datasets can be used to stress-test molecule-specific methods or transfer learning [4].

For encoding these raw input features, the dataset authors prepare simple modules called AtomEncoder and BondEncoder. They can be used as follows to embed raw atom and bond features to obtain atom_emb and bond_emb.

from ogb.graphproppred.mol_encoder import AtomEncoder, BondEncoder
atom_encoder = AtomEncoder(emb_dim = 100)
bond_encoder = BondEncoder(emb_dim = 100)

atom_emb = atom_encoder(x) # x is the input atom feature
edge_emb = bond_encoder(edge_attr) # edge_attr is the input edge feature

Prediction task: The task is to predict the target molecular properties as accurately as possible, where the molecular properties are cast as binary labels, e.g, whether a molecule inhibits HIV virus replication or not. Note that some datasets (e.g., ogbg-molpcba) can have multiple tasks, and can contain nan that indicates the corresponding label is not assigned to the molecule. For evaluation metric, the dataset authors closely follow [2]. Specifically, for ogbg-molhiv, the dataset authors use ROC...

Inhibitory effect of antisense TcCaNA2 oligonucleotides on T. cruzi cell invasion and proliferation. (XLSX)

Graphics are very effective for communicating numerical information quickly and efficiently, but many of the design choices we make are based on subjective measures, such as personal taste or conventions of the discipline rather than objective criteria. We briefly introduce perceptual principles such as preattentive features and gestalt heuristics, and then discuss the design and results of a factorial experiment examining the effect of plot aesthetics such as color and trend lines on participants’ assessment of ambiguous data displays. The quantitative and qualitative experimental results strongly suggest that plot aesthetics have a significant impact on the perception of important features in data displays. Supplementary materials for this article are available online.

Results of the 'Assessing the Overlap of Science Knowledge Graphs: A Quantitative Analysis' papers. There are 2 datasets:

• 'exact_matches.csv': contains detailed information about the concepts present both in OpenAlex and OpenAIRE.
• 'related_matches.csv': contains detailed information about the concepts from OpenAlex and OpenAIRE that were not present in both KGs but got aligned following the algorithm presented in the paper.

The detailed information refers to the following column:

• Category1: name of the first category
• Source1: source of the first category ('OpenAlex' or 'OpenAIRE')
• Category2: name of the second category
• Source2: source of the first category ('OpenAlex' or 'OpenAIRE')
• Similarity: semantic similarity value of the two categories
• PapersInC1: number of papers from the collected dataset belonging to the first category
• PapersInC2: number of papers from the collected dataset belonging to the second category
• PapersInBoth: number of papers from the collected dataset belonging to both of the categories
• Agreement: the value of the agreement of the categories in the tw KGs (Intersection over Union)

BackgroundPsychosis has various causes, including mania and schizophrenia. Since the differential diagnosis of psychosis is exclusively based on subjective assessments of oral interviews with patients, an objective quantification of the speech disturbances that characterize mania and schizophrenia is in order. In principle, such quantification could be achieved by the analysis of speech graphs. A graph represents a network with nodes connected by edges; in speech graphs, nodes correspond to words and edges correspond to semantic and grammatical relationships. Methodology/Principal FindingsTo quantify speech differences related to psychosis, interviews with schizophrenics, manics and normal subjects were recorded and represented as graphs. Manics scored significantly higher than schizophrenics in ten graph measures. Psychopathological symptoms such as logorrhea, poor speech, and flight of thoughts were grasped by the analysis even when verbosity differences were discounted. Binary classifiers based on speech graph measures sorted schizophrenics from manics with up to 93.8% of sensitivity and 93.7% of specificity. In contrast, sorting based on the scores of two standard psychiatric scales (BPRS and PANSS) reached only 62.5% of sensitivity and specificity. Conclusions/SignificanceThe results demonstrate that alterations of the thought process manifested in the speech of psychotic patients can be objectively measured using graph-theoretical tools, developed to capture specific features of the normal and dysfunctional flow of thought, such as divergence and recurrence. The quantitative analysis of speech graphs is not redundant with standard psychometric scales but rather complementary, as it yields a very accurate sorting of schizophrenics and manics. Overall, the results point to automated psychiatric diagnosis based not on what is said, but on how it is said.

Performance evaluation with training on graphs for and testing on graphs for N = 7 (extrapolative). The same comparative experiments as in the previous experiment were conducted. The numbers in parentheses for the proposed method indicate its rank in each column, and results outperforming the proposed method are underlined.

This file contains the data and code to reproduce the results from the paper "Reasoning over higher-order qualitative spatial relations via spatially explicit neural network". Instructions of running the code can be found at README.txt

Abstract of the paper: Qualitative spatial reasoning has been a core research topic in GIScience and AI for decades. It has been adopted in a wide range of applications such as wayfinding, question answering, and robotics. Most developed spatial inference engines use symbolic representation and reasoning, which focuses on small and densely connected data sets, and struggles to deal with noise and vagueness. However, with more sensors becoming available, reasoning over spatial relations on large-scale and noisy geospatial data sets requires more robust alternatives. This paper, therefore, proposes a subsymbolic approach using neural networks to facilitate qualitative spatial reasoning. More specifically, we focus on higher-order spatial relations as those have been largely ignored due to the binary nature of the underlying representations, e.g., knowledge graphs. We specifically explore the use of neural networks to reason over ternary projective relations such as between. We consider multiple types of spatial constraint, including higher-order relatedness and the conceptual neighborhood of ternary projective relations to make the proposed model spatially explicit. We introduce evaluating results demonstrating that the proposed spatially explicit method substantially outperforms existing baseline by about 20%.

The Maine Geological Survey and the USGS coordinate the colletction of snow measurements each winter for the Maine River Flow Advisory Commission's flood prediction report. These measurements are sent to MGS monthly in January and February and weekly in March, April and May as long as there is snow on the ground. The dataset contains all the raw snow survey measurements (depth, water content, density), their locations, data quality and other qualitative comments or observations. These measurements are used to create the snow survey site summary graphs. These graphs show the water content measurements by defined date range for the current year and the complete historical mean, minimum, maximum, and percentiles

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Index Time Series for Invesco Quantitative Strats Glbl Eq Lw Vol Lw Crbn UCITS ETF Acc EUR. The frequency of the observation is daily. Moving average series are also typically included. NA