GitHub - recite/softverse: Auto-compute Citations to Software From Replication Files

curl -LsSf https://astral.sh/uv/install.sh | sh

git clone https://github.com/recite/softverse.git
cd softverse
uv sync --all-extras

# Collect datasets using configuration file
uv run collect-datasets --config config/settings.yaml --output-dir outputs/data/datasets/

# Force refresh to re-download all datasets
uv run collect-datasets --force-refresh --output-dir outputs/data/datasets/

# Use custom CSV input file with dataverse information
uv run collect-datasets --input-csv data/dataverse_socialscience.csv --output-dir outputs/data/datasets/

# Collect scripts from all sources (Dataverse, Zenodo, ICPSR)
uv run collect-scripts --source all --base-output-dir outputs/scripts/

# Collect only from Dataverse
uv run collect-scripts --source dataverse --datasets-dir outputs/data/datasets/ --base-output-dir outputs/scripts/

# Collect from Zenodo with specific communities
uv run collect-scripts --source zenodo --zenodo-communities harvard-dataverse --max-zenodo-records 1000

# Collect from ICPSR with query
uv run collect-scripts --source icpsr --icpsr-query "political science" --max-icpsr-studies 500

# Analyze imports from collected scripts
uv run analyze-imports --scripts-dir outputs/scripts/ --output-dir outputs/analysis/

# Specify script patterns to analyze
uv run analyze-imports --scripts-dir outputs/scripts/ --output-dir outputs/analysis/ --config config/settings.yaml

# Note: OSF collector is available via Python API (see below)
from softverse.collectors import OSFCollector

# Note: ResearchBox collector is available via Python API (see below)
from softverse.collectors import ResearchBoxCollector

export DATAVERSE_API_KEY="your-dataverse-api-key"
export OSF_API_TOKEN="your-osf-personal-access-token"
export ZENODO_ACCESS_TOKEN="your-zenodo-access-token"
export ICPSR_USERNAME="your-icpsr-username"
export ICPSR_PASSWORD="your-icpsr-password"

dataverse:
  base_url: "https://dataverse.harvard.edu"
  api_key: "your-api-key-here"  # Optional, for authenticated requests
  input_csv: "data/dataverse_socialscience.csv"

osf:
  api_token: "your-osf-token-here"  # Optional, for authenticated requests
  rate_limit_delay: 0.5

zenodo:
  access_token: "your-zenodo-token"  # Optional, for authenticated requests
  communities: ["harvard-dataverse"]

icpsr:
  username: "your-username"
  password: "your-password"

researchbox:
  base_url: "https://researchbox.org"
  concurrent_downloads: 5

# Output directories
output:
  datasets_dir: "outputs/data/datasets"
  scripts_dir: "outputs/scripts"
  analysis_dir: "outputs/analysis"
  logs_dir: "outputs/logs"

from pathlib import Path
from softverse.collectors import (
    DataverseCollector,
    OSFCollector,
    ResearchBoxCollector,
    ZenodoCollector
)
from softverse.analyzers import ImportAnalyzer

# Initialize collectors
dataverse = DataverseCollector()
osf = OSFCollector()
researchbox = ResearchBoxCollector()

# Collect datasets from Harvard Dataverse
datasets = dataverse.collect_from_dataverse_csv(
    csv_path="data/dataverse_socialscience.csv",
    output_dir=Path("outputs/datasets")
)

# Search and collect from OSF
osf_results = osf.search_nodes("reproducibility")
osf.collect_nodes(
    node_ids=[node["id"] for node in osf_results[:10]],
    output_dir=Path("outputs/osf"),
    download_files=True
)

# Collect from ResearchBox
researchbox.collect_range(
    start_id=1,
    end_id=100,
    output_dir=Path("outputs/researchbox"),
    extract=True
)

# Analyze R package imports
analyzer = ImportAnalyzer()
results = analyzer.analyze_directory(
    directory=Path("outputs/scripts"),
    output_dir=Path("outputs/analysis")
)

# Get summary statistics
summary = analyzer.generate_summary_statistics(results)
print(f"Total scripts analyzed: {summary['total_files']}")
print(f"Top R packages: {summary['top_r_packages'][:10]}")
print(f"Top Python packages: {summary['top_python_packages'][:10]}")