Datasets:

quintic
/

fresh10

#!/usr/bin/env python3
"""Build cleaned Fresh10 text files and provenance metadata for five domains."""

from __future__ import annotations

import hashlib
import html
from html.parser import HTMLParser
import json
import os
from pathlib import Path
import re
import subprocess
import xml.etree.ElementTree as ET


ROOT = Path(
    os.environ.get(
        "FRESH10_SOURCE_PACK",
        Path(__file__).resolve().parent / "source_pack",
    )
)


class PLOSBodyParser(HTMLParser):
    """Extract semantic article prose from PLOS HTML, excluding non-prose matter."""

    BLOCK_TAGS = {"p", "h1", "h2", "h3", "h4", "h5", "li", "blockquote"}
    SKIP_CLASSES = {
        "figure",
        "table-wrap",
        "supplementary-material",
        "figshare_widget",
        "reflinks",
        "find-nolinks",
        "metrics-tab-content",
    }
    STOP_HEADINGS = {
        "references",
        "supporting information",
        "acknowledgments",
        "acknowledgements",
        "author contributions",
    }
    VOID_TAGS = {"area", "base", "br", "col", "embed", "hr", "img", "input", "link", "meta", "param", "source", "track", "wbr"}

    def __init__(self) -> None:
        super().__init__(convert_charrefs=True)
        self.in_article = False
        self.article_depth = 0
        self.skip_depth = 0
        self.block_depth = 0
        self.block_parts: list[str] = []
        self.blocks: list[str] = []
        self.stopped = False

    @staticmethod
    def _attrs(attrs: list[tuple[str, str | None]]) -> dict[str, str]:
        return {key: value or "" for key, value in attrs}

    def handle_starttag(self, tag: str, attrs: list[tuple[str, str | None]]) -> None:
        values = self._attrs(attrs)
        classes = set(values.get("class", "").split())
        if not self.in_article and tag == "div" and values.get("id") == "artText":
            self.in_article = True
            self.article_depth = 1
            return
        if not self.in_article or self.stopped:
            return
        is_void = tag in self.VOID_TAGS
        if not is_void:
            self.article_depth += 1
        if self.skip_depth:
            if not is_void:
                self.skip_depth += 1
            return
        if tag in {"script", "style", "noscript"} or classes & self.SKIP_CLASSES:
            self.skip_depth = 1
            return
        if tag in self.BLOCK_TAGS:
            if self.block_depth == 0:
                self.block_parts = []
            self.block_depth += 1
        elif tag in {"br"} and self.block_depth:
            self.block_parts.append(" ")
        elif tag == "sup" and self.block_depth:
            self.block_parts.append("^")

    def handle_endtag(self, tag: str) -> None:
        if not self.in_article or self.stopped:
            return
        if self.skip_depth:
            self.skip_depth -= 1
        elif tag in self.BLOCK_TAGS and self.block_depth:
            self.block_depth -= 1
            if self.block_depth == 0:
                block = normalize_inline("".join(self.block_parts))
                if block.casefold().rstrip(":") in self.STOP_HEADINGS:
                    self.stopped = True
                elif block:
                    self.blocks.append(block)
                self.block_parts = []
        if tag not in self.VOID_TAGS:
            self.article_depth -= 1
            if self.article_depth == 0:
                self.in_article = False

    def handle_data(self, data: str) -> None:
        if self.in_article and not self.stopped and not self.skip_depth and self.block_depth:
            self.block_parts.append(data)


def normalize_inline(value: str) -> str:
    value = html.unescape(value).replace("\u00a0", " ").replace("\u200b", "")
    value = re.sub(r"\s+", " ", value).strip()
    # Drop PLOS's inline citation-number remnants while retaining substantive numbers.
    value = re.sub(r"\s*\[(?:\d+(?:\s*[–-]\s*\d+)?)(?:\s*,\s*\d+)*\]", "", value)
    return value


def clean_plos(raw_path: Path, out_path: Path) -> None:
    parser = PLOSBodyParser()
    parser.feed(raw_path.read_text(encoding="utf-8"))
    blocks: list[str] = []
    for block in parser.blocks:
        # These metadata paragraphs and embedded captions are not article prose.
        if block.startswith(("Citation:", "Editor:", "Received:", "Copyright:", "Data Availability:", "Funding:", "Competing interests:")):
            continue
        if re.match(r"^(Fig(?:ure)?|Table)\s+\d+[.:]", block, flags=re.I):
            continue
        if block in {"Article Metrics", "Reader Comments", "Top", "Print"}:
            continue
        blocks.append(block)
    out_path.write_text("\n\n".join(blocks).strip() + "\n", encoding="utf-8")


def clean_crs(raw_path: Path, out_path: Path, title: str, page_ranges: list[tuple[int, int]]) -> None:
    """Extract CRS prose by font geometry, omitting small-print notes and visual matter."""
    paragraphs: list[str] = []
    for start_page, end_page in page_ranges:
        result = subprocess.run(
            ["pdftohtml", "-f", str(start_page), "-l", str(end_page), "-i", "-xml", "-stdout", str(raw_path)],
            check=True,
            stdout=subprocess.PIPE,
            stderr=subprocess.DEVNULL,
        )
        tree = ET.fromstring(result.stdout)
        font_sizes: dict[str, int] = {}
        for page in tree.findall("page"):
            font_sizes.update(
                {
                    spec.attrib["id"]: int(spec.attrib["size"])
                    for spec in page.findall("fontspec")
                }
            )
            fragments: list[tuple[int, int, int, str]] = []
            for item in page.findall("text"):
                size = font_sizes.get(item.attrib.get("font", ""), 0)
                top = int(item.attrib.get("top", "0"))
                left = int(item.attrib.get("left", "0"))
                # Body prose is 15-16 pt in pdftohtml coordinates. Footnotes,
                # sources, captions, table cells, headers, and footers are <=13 pt.
                if size < 15 or top < 100 or top > 1100:
                    continue
                value = normalize_inline("".join(item.itertext()))
                if value:
                    fragments.append((top, left, size, value))
            fragments.sort()

            lines: list[tuple[int, int, str]] = []
            for top, left, size, value in fragments:
                if lines and abs(lines[-1][0] - top) <= 2:
                    old_top, old_size, old_value = lines[-1]
                    lines[-1] = (old_top, max(old_size, size), normalize_inline(old_value + " " + value))
                else:
                    lines.append((top, size, value))

            current: list[str] = []
            previous_top: int | None = None
            for top, size, value in lines:
                is_heading = size >= 18
                if is_heading or (previous_top is not None and top - previous_top > 25):
                    if current:
                        paragraphs.append(normalize_inline(" ".join(current)))
                        current = []
                if is_heading:
                    paragraphs.append(value)
                else:
                    current.append(value)
                previous_top = top
            if current:
                paragraphs.append(normalize_inline(" ".join(current)))

    kept: list[str] = []
    for para in paragraphs:
        if para in {title, "Congressional Research Service"}:
            continue
        if re.match(r"^(?:Table|Figure)\s+\d+[A-Za-z]?[.:]", para):
            continue
        if para.startswith(("Source:", "Notes:", "Note:")):
            continue
        if re.fullmatch(r"(?:[-−]?\$?[\d,.%]+\s*)+", para):
            continue
        kept.append(para)

    out_path.write_text(title + "\n\n" + "\n\n".join(kept).strip() + "\n", encoding="utf-8")


DOCS = {
    "history": [
        ("dead-sea-scrolls", "Dating ancient manuscripts using radiocarbon and AI-based writing style analysis", "PLOS ONE", "https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0323185", "2025-06-04"),
        ("thera-eruption", "The Minoan Thera eruption predates Pharaoh Ahmose: Radiocarbon dating of Egyptian 17th to early 18th Dynasty museum objects", "PLOS ONE", "https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0330702", "2025-09-10"),
        ("mesoamerican-cuisine", "Amorphous carbonized objects and their contribution to reconstructing ancient Mesoamerican cuisine", "PLOS ONE", "https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0334457", "2025-11-19"),
        ("mongolian-pastoralism", "3000 yr-old patterns of mobile pastoralism revealed by multiple isotopes and radiocarbon dating of ancient horses from the Mongolian Altai", "PLOS ONE", "https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0322431", "2025-05-07"),
    ],
    "climate": [
        ("urban-thermal-inequity", "Urban thermal inequity: Advancing thermal justice transitions for vulnerable low-income populations", "PLOS Climate", "https://journals.plos.org/climate/article?id=10.1371%2Fjournal.pclm.0000968", "2026-06-18"),
        ("climate-hygiene-roadmap", "Climate change, hygiene, and health: A research roadmap for climate adaptation", "PLOS Climate", "https://journals.plos.org/climate/article?id=10.1371%2Fjournal.pclm.0000907", "2026-05-11"),
        ("india-post-ar6", "A post-AR6 update on observed and projected climate change in India", "PLOS Climate", "https://journals.plos.org/climate/article?id=10.1371%2Fjournal.pclm.0000724", "2025-11-20"),
        ("multidecadal-variability", "Comparison of multidecadal variability in climate reanalyses and global models", "PLOS Climate", "https://journals.plos.org/climate/article?id=10.1371%2Fjournal.pclm.0000519", "2025-10-07"),
    ],
    "education": [
        ("sports-ed-dual-path", "A dual-path framework for enhancing student engagement and learning outcomes in sports education", "PLOS ONE", "https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0345809", "2026-03-25"),
        ("online-learning-cognitive-load", "Cognitive load and pedagogical tension in multi-platform online learning: Evidence from Chinese higher education", "PLOS ONE", "https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0347566", "2026-04-21"),
        ("genai-design-education", "Teacher support for generative AI use and design learning outcomes in fashion design education", "PLOS ONE", "https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0347428", "2026-05-06"),
        ("student-achievement-ml", "What factors enhance students' achievement? A machine learning and interpretable methods approach", "PLOS ONE", "https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0323345", "2025-05-16"),
    ],
    "medicine": [
        ("pneumonia-hospitalisation", "Prediction of hospitalisation in young children with pneumonia in Malawi: A machine learning-based approach", "PLOS Medicine", "https://journals.plos.org/plosmedicine/article?id=10.1371%2Fjournal.pmed.1005122", "2026-06-09"),
        ("lung-allograft-multiomics", "Multi-omics biomarkers of endothelial dysregulation preceding chronic lung allograft dysfunction", "PLOS Medicine", "https://journals.plos.org/plosmedicine/article?id=10.1371%2Fjournal.pmed.1004725", "2026-06-23"),
        ("statins-type2-diabetes", "Cardiovascular outcomes and safety associated with statin therapy for primary prevention in older adults with type 2 diabetes", "PLOS Medicine", "https://journals.plos.org/plosmedicine/article?id=10.1371%2Fjournal.pmed.1005136", "2026-06-24"),
        ("retinal-neurodegeneration", "Plasma proteomic signatures of early retinal neurodegeneration in diabetes: a multi-cohort study", "PLOS Medicine", "https://journals.plos.org/plosmedicine/article?id=10.1371%2Fjournal.pmed.1004868", "2026-06-02"),
    ],
}

CRS_DOCS = [
    ("congressional-commissions", "Congressional Commissions: Overview and Considerations for Congress", "https://www.congress.gov/crs_external_products/R/PDF/R40076/R40076.31.pdf", "2026-01-21", [(5, 11)]),
    ("federal-reserve-policy", "Federal Reserve: Policy Issues in the 119th Congress", "https://www.congress.gov/crs_external_products/R/PDF/R48390/R48390.6.pdf", "2026-01-14", [(4, 15)]),
    ("universal-service-fund", "The Universal Service Fund and Related FCC Broadband Programs: Overview and Considerations for Congress", "https://www.congress.gov/crs_external_products/R/PDF/R47621/R47621.10.pdf", "2026-01-21", [(5, 6), (8, 15)]),
    ("federal-rd-funding", "Federal Research and Development (R&D) Funding: FY2026", "https://www.congress.gov/crs_external_products/R/PDF/R48694/R48694.9.pdf", "2025-12-23", [(5, 18)]),
]

# One third-party quotation appears in the selected CRS pages. Remove its whole
# containing sentence rather than publishing quoted text or a synthetic paraphrase.
CRS_THIRD_PARTY_SENTENCES = {
    "federal-reserve-policy": [
        r"At a press conference on January 29, 2025, Chair Powell said,\s*“[^”]+”\s*",
        r"In response, Chair Powell made a statement where he said that\s*",
    ],
}


def digest(path: Path) -> str:
    return hashlib.sha256(path.read_bytes()).hexdigest()


def record_for_text(
    *,
    domain: str,
    slug: str,
    title: str,
    publisher: str,
    url: str,
    date: str,
    license_name: str,
    license_url: str,
    raw: Path,
    text: Path,
    notes: str,
) -> dict[str, object]:
    return {
        "domain": domain,
        "id": slug,
        "title": title,
        "publisher": publisher,
        "source_url": url,
        "published_date": date,
        "retrieved_date": "2026-08-12",
        "license": {"name": license_name, "url": license_url},
        "raw_file": str(raw.relative_to(ROOT)),
        "text_file": str(text.relative_to(ROOT)),
        "raw_sha256": digest(raw),
        "text_sha256": digest(text),
        "extraction_notes": notes,
    }


def main() -> None:
    records: list[dict[str, object]] = []
    for domain, docs in DOCS.items():
        domain_dir = ROOT / domain
        for slug, title, publisher, url, date in docs:
            raw = domain_dir / "raw" / f"{slug}.html"
            text = domain_dir / f"{slug}.txt"
            clean_plos(raw, text)
            records.append(record_for_text(
                domain=domain,
                slug=slug,
                title=title,
                publisher=publisher,
                url=url,
                date=date,
                license_name="Creative Commons Attribution 4.0 International",
                license_url="https://creativecommons.org/licenses/by/4.0/",
                raw=raw,
                text=text,
                notes="Parsed the PLOS #artText body; retained headings and prose; removed site chrome, article metadata, references, supporting information, figures, tables, captions, and inline citation markers.",
            ))
    domain_dir = ROOT / "government"
    for slug, title, url, date, page_ranges in CRS_DOCS:
        raw = domain_dir / "raw" / f"{slug}.pdf"
        text = domain_dir / f"{slug}.txt"
        clean_crs(raw, text, title, page_ranges)
        cleaned = text.read_text(encoding="utf-8")
        for sentence in CRS_THIRD_PARTY_SENTENCES.get(slug, []):
            cleaned, replacements = re.subn(sentence, "", cleaned)
            if replacements != 1:
                raise RuntimeError(
                    f"{slug}: expected one third-party sentence, removed {replacements}"
                )
        text.write_text(cleaned, encoding="utf-8")
        records.append(record_for_text(
            domain="government",
            slug=slug,
            title=title,
            publisher="Congressional Research Service / Library of Congress",
            url=url,
            date=date,
            license_name="United States government work (public domain in the United States)",
            license_url="https://www.congress.gov/crs-products",
            raw=raw,
            text=text,
            notes=f"Converted selected report page ranges {page_ranges} with pdftohtml XML; retained body/heading font geometry and removed small-print footnotes/references, repeated headers/footers, figures, tables, captions, and sources. Selected pages omit cover, contents, and end matter. Removed the whole sentence containing one identified third-party quotation from the Federal Reserve selection.",
        ))
    (ROOT / "sources.json").write_text(
        json.dumps({"schema_version": 1, "retrieved_date": "2026-08-12", "documents": records}, indent=2, ensure_ascii=False) + "\n",
        encoding="utf-8",
    )


if __name__ == "__main__":
    main()

	#!/usr/bin/env python3
	"""Build cleaned Fresh10 text files and provenance metadata for five domains."""

	from __future__ import annotations

	import hashlib
	import html
	from html.parser import HTMLParser
	import json
	import os
	from pathlib import Path
	import re
	import subprocess
	import xml.etree.ElementTree as ET


	ROOT = Path(
	os.environ.get(
	"FRESH10_SOURCE_PACK",
	Path(__file__).resolve().parent / "source_pack",
	)
	)


	class PLOSBodyParser(HTMLParser):
	"""Extract semantic article prose from PLOS HTML, excluding non-prose matter."""

	BLOCK_TAGS = {"p", "h1", "h2", "h3", "h4", "h5", "li", "blockquote"}
	SKIP_CLASSES = {
	"figure",
	"table-wrap",
	"supplementary-material",
	"figshare_widget",
	"reflinks",
	"find-nolinks",
	"metrics-tab-content",
	}
	STOP_HEADINGS = {
	"references",
	"supporting information",
	"acknowledgments",
	"acknowledgements",
	"author contributions",
	}
	VOID_TAGS = {"area", "base", "br", "col", "embed", "hr", "img", "input", "link", "meta", "param", "source", "track", "wbr"}

	def __init__(self) -> None:
	super().__init__(convert_charrefs=True)
	self.in_article = False
	self.article_depth = 0
	self.skip_depth = 0
	self.block_depth = 0
	self.block_parts: list[str] = []
	self.blocks: list[str] = []
	self.stopped = False

	@staticmethod
	def _attrs(attrs: list[tuple[str, str \| None]]) -> dict[str, str]:
	return {key: value or "" for key, value in attrs}

	def handle_starttag(self, tag: str, attrs: list[tuple[str, str \| None]]) -> None:
	values = self._attrs(attrs)
	classes = set(values.get("class", "").split())
	if not self.in_article and tag == "div" and values.get("id") == "artText":
	self.in_article = True
	self.article_depth = 1
	return
	if not self.in_article or self.stopped:
	return
	is_void = tag in self.VOID_TAGS
	if not is_void:
	self.article_depth += 1
	if self.skip_depth:
	if not is_void:
	self.skip_depth += 1
	return
	if tag in {"script", "style", "noscript"} or classes & self.SKIP_CLASSES:
	self.skip_depth = 1
	return
	if tag in self.BLOCK_TAGS:
	if self.block_depth == 0:
	self.block_parts = []
	self.block_depth += 1
	elif tag in {"br"} and self.block_depth:
	self.block_parts.append(" ")
	elif tag == "sup" and self.block_depth:
	self.block_parts.append("^")

	def handle_endtag(self, tag: str) -> None:
	if not self.in_article or self.stopped:
	return
	if self.skip_depth:
	self.skip_depth -= 1
	elif tag in self.BLOCK_TAGS and self.block_depth:
	self.block_depth -= 1
	if self.block_depth == 0:
	block = normalize_inline("".join(self.block_parts))
	if block.casefold().rstrip(":") in self.STOP_HEADINGS:
	self.stopped = True
	elif block:
	self.blocks.append(block)
	self.block_parts = []
	if tag not in self.VOID_TAGS:
	self.article_depth -= 1
	if self.article_depth == 0:
	self.in_article = False

	def handle_data(self, data: str) -> None:
	if self.in_article and not self.stopped and not self.skip_depth and self.block_depth:
	self.block_parts.append(data)


	def normalize_inline(value: str) -> str:
	value = html.unescape(value).replace("\u00a0", " ").replace("\u200b", "")
	value = re.sub(r"\s+", " ", value).strip()
	# Drop PLOS's inline citation-number remnants while retaining substantive numbers.
	value = re.sub(r"\s\[(?:\d+(?:\s[–-]\s\d+)?)(?:\s,\s\d+)\]", "", value)
	return value


	def clean_plos(raw_path: Path, out_path: Path) -> None:
	parser = PLOSBodyParser()
	parser.feed(raw_path.read_text(encoding="utf-8"))
	blocks: list[str] = []
	for block in parser.blocks:
	# These metadata paragraphs and embedded captions are not article prose.
	if block.startswith(("Citation:", "Editor:", "Received:", "Copyright:", "Data Availability:", "Funding:", "Competing interests:")):
	continue
	if re.match(r"^(Fig(?:ure)?\|Table)\s+\d+[.:]", block, flags=re.I):
	continue
	if block in {"Article Metrics", "Reader Comments", "Top", "Print"}:
	continue
	blocks.append(block)
	out_path.write_text("\n\n".join(blocks).strip() + "\n", encoding="utf-8")


	def clean_crs(raw_path: Path, out_path: Path, title: str, page_ranges: list[tuple[int, int]]) -> None:
	"""Extract CRS prose by font geometry, omitting small-print notes and visual matter."""
	paragraphs: list[str] = []
	for start_page, end_page in page_ranges:
	result = subprocess.run(
	["pdftohtml", "-f", str(start_page), "-l", str(end_page), "-i", "-xml", "-stdout", str(raw_path)],
	check=True,
	stdout=subprocess.PIPE,
	stderr=subprocess.DEVNULL,
	)
	tree = ET.fromstring(result.stdout)
	font_sizes: dict[str, int] = {}
	for page in tree.findall("page"):
	font_sizes.update(
	{
	spec.attrib["id"]: int(spec.attrib["size"])
	for spec in page.findall("fontspec")
	}
	)
	fragments: list[tuple[int, int, int, str]] = []
	for item in page.findall("text"):
	size = font_sizes.get(item.attrib.get("font", ""), 0)
	top = int(item.attrib.get("top", "0"))
	left = int(item.attrib.get("left", "0"))
	# Body prose is 15-16 pt in pdftohtml coordinates. Footnotes,
	# sources, captions, table cells, headers, and footers are <=13 pt.
	if size < 15 or top < 100 or top > 1100:
	continue
	value = normalize_inline("".join(item.itertext()))
	if value:
	fragments.append((top, left, size, value))
	fragments.sort()

	lines: list[tuple[int, int, str]] = []
	for top, left, size, value in fragments:
	if lines and abs(lines[-1][0] - top) <= 2:
	old_top, old_size, old_value = lines[-1]
	lines[-1] = (old_top, max(old_size, size), normalize_inline(old_value + " " + value))
	else:
	lines.append((top, size, value))

	current: list[str] = []
	previous_top: int \| None = None
	for top, size, value in lines:
	is_heading = size >= 18
	if is_heading or (previous_top is not None and top - previous_top > 25):
	if current:
	paragraphs.append(normalize_inline(" ".join(current)))
	current = []
	if is_heading:
	paragraphs.append(value)
	else:
	current.append(value)
	previous_top = top
	if current:
	paragraphs.append(normalize_inline(" ".join(current)))

	kept: list[str] = []
	for para in paragraphs:
	if para in {title, "Congressional Research Service"}:
	continue
	if re.match(r"^(?:Table\|Figure)\s+\d+[A-Za-z]?[.:]", para):
	continue
	if para.startswith(("Source:", "Notes:", "Note:")):
	continue
	if re.fullmatch(r"(?:[-−]?\$?[\d,.%]+\s*)+", para):
	continue
	kept.append(para)

	out_path.write_text(title + "\n\n" + "\n\n".join(kept).strip() + "\n", encoding="utf-8")


	DOCS = {
	"history": [
	("dead-sea-scrolls", "Dating ancient manuscripts using radiocarbon and AI-based writing style analysis", "PLOS ONE", "https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0323185", "2025-06-04"),
	("thera-eruption", "The Minoan Thera eruption predates Pharaoh Ahmose: Radiocarbon dating of Egyptian 17th to early 18th Dynasty museum objects", "PLOS ONE", "https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0330702", "2025-09-10"),
	("mesoamerican-cuisine", "Amorphous carbonized objects and their contribution to reconstructing ancient Mesoamerican cuisine", "PLOS ONE", "https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0334457", "2025-11-19"),
	("mongolian-pastoralism", "3000 yr-old patterns of mobile pastoralism revealed by multiple isotopes and radiocarbon dating of ancient horses from the Mongolian Altai", "PLOS ONE", "https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0322431", "2025-05-07"),
	],
	"climate": [
	("urban-thermal-inequity", "Urban thermal inequity: Advancing thermal justice transitions for vulnerable low-income populations", "PLOS Climate", "https://journals.plos.org/climate/article?id=10.1371%2Fjournal.pclm.0000968", "2026-06-18"),
	("climate-hygiene-roadmap", "Climate change, hygiene, and health: A research roadmap for climate adaptation", "PLOS Climate", "https://journals.plos.org/climate/article?id=10.1371%2Fjournal.pclm.0000907", "2026-05-11"),
	("india-post-ar6", "A post-AR6 update on observed and projected climate change in India", "PLOS Climate", "https://journals.plos.org/climate/article?id=10.1371%2Fjournal.pclm.0000724", "2025-11-20"),
	("multidecadal-variability", "Comparison of multidecadal variability in climate reanalyses and global models", "PLOS Climate", "https://journals.plos.org/climate/article?id=10.1371%2Fjournal.pclm.0000519", "2025-10-07"),
	],
	"education": [
	("sports-ed-dual-path", "A dual-path framework for enhancing student engagement and learning outcomes in sports education", "PLOS ONE", "https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0345809", "2026-03-25"),
	("online-learning-cognitive-load", "Cognitive load and pedagogical tension in multi-platform online learning: Evidence from Chinese higher education", "PLOS ONE", "https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0347566", "2026-04-21"),
	("genai-design-education", "Teacher support for generative AI use and design learning outcomes in fashion design education", "PLOS ONE", "https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0347428", "2026-05-06"),
	("student-achievement-ml", "What factors enhance students' achievement? A machine learning and interpretable methods approach", "PLOS ONE", "https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0323345", "2025-05-16"),
	],
	"medicine": [
	("pneumonia-hospitalisation", "Prediction of hospitalisation in young children with pneumonia in Malawi: A machine learning-based approach", "PLOS Medicine", "https://journals.plos.org/plosmedicine/article?id=10.1371%2Fjournal.pmed.1005122", "2026-06-09"),
	("lung-allograft-multiomics", "Multi-omics biomarkers of endothelial dysregulation preceding chronic lung allograft dysfunction", "PLOS Medicine", "https://journals.plos.org/plosmedicine/article?id=10.1371%2Fjournal.pmed.1004725", "2026-06-23"),
	("statins-type2-diabetes", "Cardiovascular outcomes and safety associated with statin therapy for primary prevention in older adults with type 2 diabetes", "PLOS Medicine", "https://journals.plos.org/plosmedicine/article?id=10.1371%2Fjournal.pmed.1005136", "2026-06-24"),
	("retinal-neurodegeneration", "Plasma proteomic signatures of early retinal neurodegeneration in diabetes: a multi-cohort study", "PLOS Medicine", "https://journals.plos.org/plosmedicine/article?id=10.1371%2Fjournal.pmed.1004868", "2026-06-02"),
	],
	}

	CRS_DOCS = [
	("congressional-commissions", "Congressional Commissions: Overview and Considerations for Congress", "https://www.congress.gov/crs_external_products/R/PDF/R40076/R40076.31.pdf", "2026-01-21", [(5, 11)]),
	("federal-reserve-policy", "Federal Reserve: Policy Issues in the 119th Congress", "https://www.congress.gov/crs_external_products/R/PDF/R48390/R48390.6.pdf", "2026-01-14", [(4, 15)]),
	("universal-service-fund", "The Universal Service Fund and Related FCC Broadband Programs: Overview and Considerations for Congress", "https://www.congress.gov/crs_external_products/R/PDF/R47621/R47621.10.pdf", "2026-01-21", [(5, 6), (8, 15)]),
	("federal-rd-funding", "Federal Research and Development (R&D) Funding: FY2026", "https://www.congress.gov/crs_external_products/R/PDF/R48694/R48694.9.pdf", "2025-12-23", [(5, 18)]),
	]

	# One third-party quotation appears in the selected CRS pages. Remove its whole
	# containing sentence rather than publishing quoted text or a synthetic paraphrase.
	CRS_THIRD_PARTY_SENTENCES = {
	"federal-reserve-policy": [
	r"At a press conference on January 29, 2025, Chair Powell said,\s“[^”]+”\s",
	r"In response, Chair Powell made a statement where he said that\s*",
	],
	}


	def digest(path: Path) -> str:
	return hashlib.sha256(path.read_bytes()).hexdigest()


	def record_for_text(
	*,
	domain: str,
	slug: str,
	title: str,
	publisher: str,
	url: str,
	date: str,
	license_name: str,
	license_url: str,
	raw: Path,
	text: Path,
	notes: str,
	) -> dict[str, object]:
	return {
	"domain": domain,
	"id": slug,
	"title": title,
	"publisher": publisher,
	"source_url": url,
	"published_date": date,
	"retrieved_date": "2026-08-12",
	"license": {"name": license_name, "url": license_url},
	"raw_file": str(raw.relative_to(ROOT)),
	"text_file": str(text.relative_to(ROOT)),
	"raw_sha256": digest(raw),
	"text_sha256": digest(text),
	"extraction_notes": notes,
	}


	def main() -> None:
	records: list[dict[str, object]] = []
	for domain, docs in DOCS.items():
	domain_dir = ROOT / domain
	for slug, title, publisher, url, date in docs:
	raw = domain_dir / "raw" / f"{slug}.html"
	text = domain_dir / f"{slug}.txt"
	clean_plos(raw, text)
	records.append(record_for_text(
	domain=domain,
	slug=slug,
	title=title,
	publisher=publisher,
	url=url,
	date=date,
	license_name="Creative Commons Attribution 4.0 International",
	license_url="https://creativecommons.org/licenses/by/4.0/",
	raw=raw,
	text=text,
	notes="Parsed the PLOS #artText body; retained headings and prose; removed site chrome, article metadata, references, supporting information, figures, tables, captions, and inline citation markers.",
	))
	domain_dir = ROOT / "government"
	for slug, title, url, date, page_ranges in CRS_DOCS:
	raw = domain_dir / "raw" / f"{slug}.pdf"
	text = domain_dir / f"{slug}.txt"
	clean_crs(raw, text, title, page_ranges)
	cleaned = text.read_text(encoding="utf-8")
	for sentence in CRS_THIRD_PARTY_SENTENCES.get(slug, []):
	cleaned, replacements = re.subn(sentence, "", cleaned)
	if replacements != 1:
	raise RuntimeError(
	f"{slug}: expected one third-party sentence, removed {replacements}"
	)
	text.write_text(cleaned, encoding="utf-8")
	records.append(record_for_text(
	domain="government",
	slug=slug,
	title=title,
	publisher="Congressional Research Service / Library of Congress",
	url=url,
	date=date,
	license_name="United States government work (public domain in the United States)",
	license_url="https://www.congress.gov/crs-products",
	raw=raw,
	text=text,
	notes=f"Converted selected report page ranges {page_ranges} with pdftohtml XML; retained body/heading font geometry and removed small-print footnotes/references, repeated headers/footers, figures, tables, captions, and sources. Selected pages omit cover, contents, and end matter. Removed the whole sentence containing one identified third-party quotation from the Federal Reserve selection.",
	))
	(ROOT / "sources.json").write_text(
	json.dumps({"schema_version": 1, "retrieved_date": "2026-08-12", "documents": records}, indent=2, ensure_ascii=False) + "\n",
	encoding="utf-8",
	)


	if __name__ == "__main__":
	main()