better README

README.md

@@ -32,41 +32,63 @@ task_ids:
 - language-modeling
 ---
 
-# Dataset Card for Dataset Name
+# Dataset Card for Europeana Newspapers
 
+## Dataset Overview
 
-This dataset contains historic newspapers from [Europeana](https://pro.europeana.eu/page/iiif#download). In total the collection has ~32 Billion tokens. Documentation for this dataset is a WIP. 
+This dataset contains historic newspapers from [Europeana](https://pro.europeana.eu/page/iiif#download), processed and converted to a format more suitable for machine learning and digital humanities research. In total, the collection contains approximately 32 billion tokens across multiple European languages, spanning from the 18th to the early 20th century.
 
+Created by the BigLAM initiative, this unofficial version extracts text content from ALTO XML and converts it into a parquet format, making it more accessible for ML/AI-based work and large-scale digital humanities/history research.
 
+### Key Features
 
-This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
+- **Massive historical corpus**: One of the largest collections of historical text data available in a machine-learning friendly format
+- **Cross-lingual coverage**: Includes 12 European languages with varying degrees of representation
+- **OCR quality metrics**: Contains confidence scores to allow filtering based on text quality
+- **Rich metadata**: Preserves publication information, dates, and links to original materials
+- **Structured format**: Organized by language and decade for efficient access to specific subsets
+- **Illustration data**: Includes bounding box coordinates for visual elements on newspaper pages
+- **IIIF integration**: Direct links to high-quality images of the original documents
 
 ## Dataset Details
 
 ### Dataset Description
 
-
-- **Curated by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
+- **Curated by:** BigLAM initiative
+- **Language(s):** German (de), French (fr), Greek (el), Estonian (et), Finnish (fi), Croatian (hr), Yiddish (ji), Polish (pl), Russian (ru), Serbian (sr), Swedish (sv), Ukrainian (uk)
 - **License:** [More Information Needed]
 
-### Dataset Sources [optional]
+### Dataset Structure
 
-<!-- Provide the basic links for the dataset. -->
+Each record in the dataset contains the following fields:
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+| Field | Type | Description |
+|-------|------|-------------|
+| **text** | string | Extracted text content from each newspaper page |
+| **mean_ocr** | float | Mean OCR confidence score (0-1 scale where higher values indicate higher confidence) |
+| **std_ocr** | float | Standard deviation of OCR confidence scores (indicates consistency of recognition quality) |
+| **bounding_boxes** | list of lists | Coordinates for illustrations on the page in format [HEIGHT, WIDTH, VPOS, HPOS] |
+| **title** | string | Newspaper title |
+| **date** | string | Publication date in ISO format (YYYY-MM-DD) |
+| **language** | list | Language codes of the content (supports multi-language detection) |
+| **item_iiif_url** | string | IIIF URL for accessing the original digitized image |
+| **multi_language** | boolean | Flag indicating whether the page contains multiple languages |
+| **issue_uri** | string | Persistent URI for the newspaper issue in Europeana |
+| **id** | string | Unique identifier combining issue URI and page number |
 
-## Uses
+### Data Splits
+
+The dataset is organized into files by:
+- Language (e.g., 'fr' for French, 'de' for German)
+- Decade (e.g., '1770' for newspapers from the 1770s)
 
-<!-- Address questions around how the dataset is intended to be used. -->
+This organization allows researchers to easily access specific subsets of the data relevant to their research questions.
+
+## Uses
 
 ### Direct Use
 
-To download the full dataset using the `Datasets` library you can do the following
+To download the full dataset using the `Datasets` library:
 
 ```python
 from datasets import load_dataset
@@ -74,157 +96,185 @@ from datasets import load_dataset
 dataset = load_dataset("biglam/europeana_newspapers")
 ```
 
-You can also access a subset based on language or decade ranges using the following function. 
+You can also access a subset based on language or year ranges using the following function:
 
 ```python
 from typing import List, Optional, Literal, Union
 from huggingface_hub import hf_hub_url, list_repo_files
 
 LanguageOption = Literal[
-    "et",
-    "pl",
-    "sr",
-    "ru",
-    "sv",
+    "et",  # Estonian
+    "pl",  # Polish
+    "sr",  # Serbian
+    "ru",  # Russian
+    "sv",  # Swedish
     "no_language_found",
-    "ji",
-    "hr",
-    "el",
-    "uk",
-    "fr",
-    "fi",
-    "de",
-    "multi_language",
+    "ji",  # Yiddish
+    "hr",  # Croatian
+    "el",  # Greek
+    "uk",  # Ukrainian
+    "fr",  # French
+    "fi",  # Finnish
+    "de",  # German
+    "multi_language"
 ]
 
 
 def get_files_for_lang_and_years(
-    languages: Union[None, List[LanguageOption]] = None,
+    languages: Optional[List[LanguageOption]] = None,
     min_year: Optional[int] = None,
     max_year: Optional[int] = None,
 ):
+    """
+    Get dataset file URLs filtered by language and/or year range.
+    
+    Args:
+        languages: List of language codes to include
+        min_year: Minimum year to include (inclusive)
+        max_year: Maximum year to include (inclusive)
+        
+    Returns:
+        List of file URLs that can be passed to load_dataset
+    """
+    # List all files in the repository
     files = list_repo_files("biglam/europeana_newspapers", repo_type="dataset")
     parquet_files = [f for f in files if f.endswith(".parquet")]
-    parquet_files_filtered_for_lang = [
-        f for f in parquet_files if any(lang in f for lang in ["uk", "fr"])
-    ]
-    filtered_files = [
-        f
-        for f in parquet_files
-        if (min_year is None or min_year <= int(f.split("-")[1].split(".")[0]))
-        and (max_year is None or int(f.split("-")[1].split(".")[0]) <= max_year)
-    ]
+    
+    # Filter by language if specified
+    if languages:
+        parquet_files = [
+            f for f in parquet_files if any(lang in f for lang in languages)
+        ]
+    
+    # Filter by year range if specified
+    if min_year is not None or max_year is not None:
+        filtered_files = []
+        for f in parquet_files:
+            parts = f.split("-")
+            if len(parts) > 1:
+                year_part = parts[1].split(".")[0]
+                if year_part.isdigit():
+                    year = int(year_part)
+                    if (min_year is None or min_year <= year) and (max_year is None or year <= max_year):
+                        filtered_files.append(f)
+        parquet_files = filtered_files
+
+    # Convert local paths to full URLs
     return [
         hf_hub_url("biglam/europeana_newspapers", f, repo_type="dataset")
-        for f in filtered_files
+        for f in parquet_files
     ]
-
 ```
 
-This function takes a list of language codes, and a min, max value for decades you want to include. You can can use this function to get the URLs for files you want to download from the Hub:
+You can use this function to get the URLs for files you want to download from the Hub:
 
 ```python
-ds = load_dataset("parquet", data_files=get_files_for_lang_and_years(['fr']), num_proc=4)
+# Example 1: Load French newspaper data
+french_files = get_files_for_lang_and_years(['fr'])
+ds_french = load_dataset("parquet", data_files=french_files, num_proc=4)
+
+# Example 2: Load Ukrainian and French newspapers between 1900 and 1950
+historical_files = get_files_for_lang_and_years(
+    languages=['uk', 'fr'], 
+    min_year=1900, 
+    max_year=1950
+)
+ds_historical = load_dataset("parquet", data_files=historical_files, num_proc=4)
+
+# Example 3: Load all German newspapers from the 19th century
+german_19th_century = get_files_for_lang_and_years(
+    languages=['de'], 
+    min_year=1800, 
+    max_year=1899
+)
+ds_german_historical = load_dataset("parquet", data_files=german_19th_century, num_proc=4)
 ```
 
-[More Information Needed]
+### Use Cases
 
-### Out-of-Scope Use
+This dataset is particularly valuable for:
 
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
+#### Machine Learning Applications
+- Training large language models on historical texts
+- Fine-tuning models for historical language understanding
+- Developing OCR post-correction models using the confidence scores
+- Training layout analysis models using the bounding box information
 
-[More Information Needed]
+#### Digital Humanities Research
+- Cross-lingual analysis of historical newspapers
+- Studying information spread across European regions
+- Tracking cultural and political developments over time
+- Analyzing language evolution and shifts in terminology
+- Topic modeling of historical discourse
+- Named entity recognition in historical contexts
 
-## Dataset Structure
+#### Historical Research
+- Comparative analysis of news reporting across different countries
+- Studying historical events from multiple contemporary perspectives
+- Tracking the evolution of public discourse on specific topics
+- Analyzing changes in journalistic style and content over centuries
 
-<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
+#### OCR Development
+- Using the mean_ocr and std_ocr fields to assess OCR quality
+- Filtering content based on quality thresholds for specific applications
+- Benchmarking OCR improvement techniques against historical materials
 
-[More Information Needed]
+#### Institutional Uses
+- Enabling libraries and archives to provide computational access to their collections
+- Supporting searchable interfaces for digital historical collections
+- Creating teaching resources for historical linguistics and discourse analysis
 
 ## Dataset Creation
 
-### Curation Rationale
-
-<!-- Motivation for the creation of this dataset. -->
-
-[More Information Needed]
-
 ### Source Data
 
-<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
+The dataset is derived from the Europeana Newspapers collection, which contains digitized historical newspapers from various European countries. The original data is in ALTO XML format, which includes OCR text along with layout and metadata information.
 
 #### Data Collection and Processing
 
-<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
-
-[More Information Needed]
-
-#### Who are the source data producers?
-
-<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
-
-[More Information Needed]
+The BigLAM initiative developed a comprehensive processing pipeline to convert the Europeana newspaper collections from their original ALTO XML format into a structured dataset format suitable for machine learning and digital humanities research:
 
-### Annotations [optional]
+1. **ALTO XML Parsing**: Custom parsers handle various ALTO schema versions (1-5 and BnF dialect) to ensure compatibility across the entire collection.
 
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
+2. **Text Extraction**: The pipeline extracts full-text content while preserving reading order and handling special cases like hyphenated words.
 
-#### Annotation process
+3. **OCR Quality Assessment**: For each page, the system calculates:
+   - `mean_ocr`: Average confidence score of the OCR engine
+   - `std_ocr`: Standard deviation of confidence scores to indicate consistency
 
-<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
+4. **Visual Element Extraction**: The pipeline captures bounding box coordinates for illustrations and visual elements, stored in the `bounding_boxes` field.
 
-[More Information Needed]
+5. **Metadata Integration**: Each page is enriched with corresponding metadata from separate XML files:
+   - Publication title and date
+   - Language identification (including multi-language detection)
+   - IIIF URLs for accessing the original digitized images
+   - Persistent identifiers linking back to the source material
 
-#### Who are the annotators?
+6. **Parallel Processing**: The system utilizes multi-processing to efficiently handle the massive collection (containing approximately 32 billion tokens).
 
-<!-- This section describes the people or systems who created the annotations. -->
+7. **Dataset Creation**: The processed data is converted to Hugging Face's `Dataset` format and saved as parquet files, organized by language and decade for easier access.
 
-[More Information Needed]
-
-#### Personal and Sensitive Information
-
-<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
-
-[More Information Needed]
+This processing approach preserves the valuable structure and metadata of the original collection while making it significantly more accessible for computational analysis and machine learning applications.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+- **OCR Quality**: The dataset is based on OCR'd historical documents, which may contain errors, especially in older newspapers or those printed in non-standard fonts.
+- **Historical Bias**: Historical newspapers reflect the biases, prejudices, and perspectives of their time periods, which may include content that would be considered offensive by modern standards.
+- **Temporal and Geographic Coverage**: The coverage across languages, time periods, and geographic regions may be uneven.
+- **Data Completeness**: Some newspaper issues or pages may be missing or incomplete in the original Europeana collection.
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
+- Users should consider the OCR confidence scores (mean_ocr and std_ocr) when working with this data, possibly filtering out low-quality content depending on their use case.
+- Researchers studying historical social trends should be aware of the potential biases in the source material and interpret findings accordingly.
+- For applications requiring high text accuracy, additional validation or correction may be necessary.
 
-**APA:**
+## More Information
 
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Dataset Card Authors [optional]
-
-[More Information Needed]
+For more information about the original data source, visit [Europeana Newspapers](https://pro.europeana.eu/page/iiif#download).
 
 ## Dataset Card Contact
 
-[More Information Needed]
+Daniel van Strien (daniel [at] hf [dot] co)
+
+For questions about this processed version of the Europeana Newspapers dataset, please contact the BigLAM initiative representative above.