diff --git a/README.md b/README.md
index 7ca802df72ffc831bd58ebbdce6173f51fc62c89..d7726f426701f9d6c89b6796dd1861cc70760eea 100644
--- a/README.md
+++ b/README.md
@@ -8,6 +8,7 @@ task_categories:
   - text-generation
   - question-answering
   - table-question-answering
+  - text2text-generation
 pretty_name: SFD - SEC Filings Dataset (v1)
 tags:
   - finance
@@ -32,7 +33,7 @@ configs:
 
 **SFD-v1** is an open, layout-faithful reconstruction of U.S. Securities and Exchange Commission (SEC) EDGAR filings into token-efficient MultiMarkdown (MMD), targeted at long-context language modeling, financial reasoning, document understanding, and evaluation.
 
-This release covers filings from **January 2022 through June 2025** (~3.4M filings), produced by the SFD parser described in:
+This release covers filings from **January 2022 through June 2025** (~3.4M filings), produced by the EFD parser described in:
 
 > *The SEC Filings Dataset: Reconstructing U.S. Corporate and Financial Disclosures into Layout-Faithful and Token-Efficient Pretraining Data.* Anonymous Authors (NeurIPS 2026 Evaluations & Datasets Track, under review).
 
@@ -44,7 +45,7 @@ The full SFD corpus is estimated at ~500B tokens across ~18.4M filings (1994–p
 - **Source formats handled:** HTML (\~62%), XML (\~18%), plaintext (\~18%), SGML (\~2%), PDF-via-OCR (\~1%) — see paper §3.
 - **Filings:** ~3.4M parsed `.md` documents across 350+ filing types (10-K, 10-Q, 8-K, Form 4, N-PORT, 13F, 485BPOS, ABS-EE, …).
 - **License:** CC-BY-NC-4.0 (parsed). Underlying SEC filings are U.S. Government public domain.
-- **Storage:** Parquet shards with zstd-15 compression, one shard per (year, month). Token counts use the Qwen3-1.7B tokenizer. The headline ~152B figure counts the full released `parsed_md` field, including the prepended SEC/SGML filing metadata header. The regulatory body content alone is ~111B tokens; the difference is metadata such as CIK, accession, form type, filing date, period of report, and related SEC header fields.
+- **Storage:** Parquet shards with zstd-15 compression, one shard per (year, month).
 
 ## Schema
 
@@ -67,7 +68,7 @@ Each row is one parsed filing.
 ```python
 from datasets import load_dataset
 
-ds = load_dataset("anonymous-md/EDGAR_FILINGS_DATASET", split="train", streaming=True)
+ds = load_dataset("mikedd/EDGAR_FILINGS_DATASET", split="train", streaming=True)
 for row in ds.take(1):
     print(row["accession"], row["year"], row["month"], len(row["parsed_md"]))
 ```
@@ -75,7 +76,7 @@ for row in ds.take(1):
 For a full local materialization (~50 GB on disk):
 
 ```python
-ds = load_dataset("anonymous-md/EDGAR_FILINGS_DATASET", split="train")
+ds = load_dataset("mikedd/EDGAR_FILINGS_DATASET", split="train")
 ds = ds.filter(lambda r: r["year"] == 2024)  # or by month, source_format, etc.
 ```
 
@@ -87,21 +88,6 @@ acc_clean = acc.replace("-", "")
 url = f"https://www.sec.gov/Archives/edgar/data/{int(acc.split('-')[0])}/{acc_clean}/"
 ```
 
-## Sample subset (for reviewer inspection)
-
-Because the full corpus exceeds 4 GB, a small sample is provided at
-`sample/sfd-v1-sample.parquet` to allow quick inspection of data quality without
-materializing the full dataset.
-
-- **Path:** `sample/sfd-v1-sample.parquet`
-- **Sampling methodology:** stratified by year — first/last representative month
-  of each year in the release (2022-01, 2023-01, 2024-01, 2025-01, 2025-06),
-  with the first 25 rows from each, sorted by accession. This surfaces a mix of
-  source formats (HTML / XML / SGML / PDF-OCR) and form types (Form 4, 10-Q,
-  8-K, NPORT-P, etc.) representative of the full corpus.
-- **Loading:** `pq.read_table("sample/sfd-v1-sample.parquet")` with the same
-  schema as the full dataset.
-
 ## Methodology summary
 
 SFD treats filings as 2-D rendered grids rather than DOM trees:
@@ -127,7 +113,7 @@ Released separately when scoring is finalized.
 ## Citation
 
 ```bibtex
-@inproceedings{anonymous2026sfd,
+@inproceedings{bettencourt2026sfd,
   title={The SEC Filings Dataset: Reconstructing U.S. Corporate and Financial Disclosures into Layout-Faithful and Token-Efficient Pretraining Data},
   author={Anonymous Authors},
   booktitle={Advances in Neural Information Processing Systems (NeurIPS), Evaluations \& Datasets Track},
diff --git a/croissant.json b/croissant.json
index 34f4d8d75bbd1b966d3b6b4575032056130f8cfa..ddd65e0309d11bb1f3cec4c5453aa7328fe06068 100644
--- a/croissant.json
+++ b/croissant.json
@@ -7,7 +7,6 @@
     "conformsTo": "dct:conformsTo",
     "cr": "http://mlcommons.org/croissant/",
     "rai": "http://mlcommons.org/croissant/RAI/",
-    "prov": "http://www.w3.org/ns/prov#",
     "data": {
       "@id": "cr:data",
       "@type": "@json"
@@ -47,27 +46,45 @@
   },
   "@type": "sc:Dataset",
   "name": "SFD",
-  "description": "The SEC Filings Dataset (SFD-v1) is an open, layout-faithful reconstruction of U.S. SEC EDGAR filings into token-efficient MultiMarkdown. This release covers ~3.4M filings filed between January 2022 and June 2025, parsed from heterogeneous HTML, XML, plaintext, SGML, and PDF source formats. Tables are reconstructed using MultiMarkdown span notation (`||` for column spans, `^^` for row spans), preserving merged cells, multi-row headers, and indentation hierarchies that standard text extraction destroys. SFD-v1 supports long-context language modeling, financial reasoning, document understanding, and evaluation. The full SFD corpus is estimated at ~500B tokens across ~18.4M filings (1994-present); this release is a public snapshot of the most recent 4.5 years.",
+  "description": "The Stanford EDGAR Filings Dataset (SFD-v1) is an open, layout-faithful reconstruction of U.S. SEC EDGAR filings into token-efficient MultiMarkdown. This release covers ~3.4M filings filed between January 2022 and June 2025, parsed from heterogeneous HTML, XML, plaintext, SGML, and PDF source formats. Tables are reconstructed using MultiMarkdown span notation (`||` for column spans, `^^` for row spans), preserving merged cells, multi-row headers, and indentation hierarchies that standard text extraction destroys. SFD-v1 supports long-context language modeling, financial reasoning, document understanding, and evaluation. The full SFD corpus is estimated at ~500B tokens across ~18.4M filings (1994-present); this release is a public snapshot of the most recent 4.5 years.",
   "conformsTo": "http://mlcommons.org/croissant/1.0",
   "license": "https://creativecommons.org/licenses/by-nc/4.0/",
-  "url": "https://huggingface.co/datasets/anonymous-md/EDGAR_FILINGS_DATASET",
+  "url": "https://huggingface.co/datasets/mikedd/EDGAR_FILINGS_DATASET",
   "version": "1.0.0",
   "datePublished": "2026-05-06",
   "creator": [
     {
       "@type": "Person",
-      "name": "Anonymous Authors",
+      "name": "Nick Bettencourt",
       "affiliation": {
         "@type": "Organization",
-        "name": "Anonymous Organization"
+        "name": "University of California, Los Angeles, Department of Applied Statistics and Data Science"
+      },
+      "email": "nbetts@g.ucla.edu"
+    },
+    {
+      "@type": "Person",
+      "name": "Xiaowei Ding",
+      "affiliation": {
+        "@type": "Organization",
+        "name": "Stanford University, Advanced Financial Technologies Lab"
       }
+    },
+    {
+      "@type": "Person",
+      "name": "Kay Giesecke",
+      "affiliation": {
+        "@type": "Organization",
+        "name": "Stanford University, Advanced Financial Technologies Lab"
+      },
+      "email": "giesecke@stanford.edu"
     }
   ],
   "publisher": {
     "@type": "Organization",
-    "name": "Anonymous Organization"
+    "name": "Stanford Advanced Financial Technologies Lab"
   },
-  "citeAs": "@inproceedings{Anonymous2026sfd, title={The SEC Filings Dataset: Reconstructing U.S. Corporate and Financial Disclosures into Layout-Faithful and Token-Efficient Pretraining Data}, author={Anonymous Authors}, booktitle={Advances in Neural Information Processing Systems (NeurIPS), Evaluations and Datasets Track}, year={2026}}",
+  "citeAs": "@inproceedings{bettencourt2026sfd, title={The SEC Filings Dataset: Reconstructing U.S. Corporate and Financial Disclosures into Layout-Faithful and Token-Efficient Pretraining Data}, author={Bettencourt, Nick and Ding, Xiaowei and Giesecke, Kay}, booktitle={Advances in Neural Information Processing Systems (NeurIPS), Evaluations and Datasets Track}, year={2026}}",
   "keywords": [
     "finance",
     "sec",
@@ -90,7 +107,7 @@
       "@id": "repo",
       "name": "repo",
       "description": "Hugging Face dataset git repository hosting SFD-v1.",
-      "contentUrl": "https://huggingface.co/datasets/anonymous-md/EDGAR_FILINGS_DATASET",
+      "contentUrl": "https://huggingface.co/datasets/mikedd/EDGAR_FILINGS_DATASET",
       "encodingFormat": "git+https",
       "sha256": "https://github.com/mlcommons/croissant/issues/80"
     },
@@ -223,26 +240,5 @@
   "rai:annotationsPerItem": "0 (no human annotation)",
   "rai:annotatorDemographics": "N/A - automated parsing only.",
   "rai:dataSocialImpact": "Lowers the barrier to studying U.S. corporate and financial disclosure at scale, supports auditing of LLMs on real-world regulatory text, and provides an open alternative to closed financial corpora.",
-  "rai:dataBiases": "Coverage is U.S.-centric and skewed toward issuers active in U.S. capital markets. Filing-type token distribution is highly non-uniform (the top 5 form types account for ~35% of tokens). Recent years are over-represented relative to the full EDGAR archive in this v1 release.",
-  "rai:hasSyntheticData": false,
-  "prov:wasDerivedFrom": [
-    {
-      "@type": "prov:Entity",
-      "name": "U.S. SEC EDGAR public archive",
-      "url": "https://www.sec.gov/edgar"
-    }
-  ],
-  "prov:wasGeneratedBy": [
-    {
-      "@type": "prov:Activity",
-      "name": "SFD parser pipeline",
-      "description": "Format-routed parsing pipeline that reconstructs SEC filings from heterogeneous HTML/XML/plaintext/SGML/PDF source formats into layout-faithful MultiMarkdown. Source release will accompany paper acceptance."
-    },
-    {
-      "@type": "prov:Activity",
-      "name": "Mistral OCR 3 (PDF subset only)",
-      "description": "Used to OCR a small minority (~1% of corpus, has_ocr=true) of PDF-only filings. Outputs subject to Mistral AI Terms of Service.",
-      "url": "https://mistral.ai/terms/"
-    }
-  ]
+  "rai:dataBiases": "Coverage is U.S.-centric and skewed toward issuers active in U.S. capital markets. Filing-type token distribution is highly non-uniform (the top 5 form types account for ~35% of tokens). Recent years are over-represented relative to the full EDGAR archive in this v1 release."
 }
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diff --git a/datasheet.md b/datasheet.md
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--- a/datasheet.md
+++ b/datasheet.md
@@ -1,4 +1,4 @@
-# Datasheet: SFD — SEC Filings Dataset (v1)
+# Datasheet: SFD — Stanford EDGAR Filings Dataset (v1)
 
 Following the *Datasheets for Datasets* template (Gebru et al., 2021).
 
@@ -8,10 +8,10 @@ Following the *Datasheets for Datasets* template (Gebru et al., 2021).
 SFD-v1 was created to provide a large, open, layout-faithful corpus of U.S. SEC EDGAR regulatory filings for long-context language modeling, financial reasoning research, document understanding, and contamination-resistant evaluation. Existing financial corpora (BloombergGPT/FinPile, BeanCounter, EDGAR-CORPUS) are either proprietary, narrowly scoped, or strip the visual table structure that financial reasoning depends on.
 
 **Who created the dataset?**
-Anonymous Authors (Anonymous Organization).
+Nick Bettencourt (UCLA Department of Applied Statistics and Data Science); Xiaowei Ding and Kay Giesecke (Stanford Advanced Financial Technologies Lab).
 
 **Who funded the creation?**
-Anonymous Organization.
+Stanford Advanced Financial Technologies Lab.
 
 ## Composition
 
@@ -76,17 +76,13 @@ By downloading filings via the SEC EDGAR public archive (https://www.sec.gov/edg
 Custom Python downloader respecting SEC's 10-requests-per-second guidance, with periodic master-index reconciliation.
 
 **If the dataset is a sample, what was the sampling strategy?**
-Not a sample — exhaustive over the date range. A small *review sample* is provided
-separately at `sample/sfd-v1-sample.parquet` (125 rows from 5 representative
-months: 2022-01, 2023-01, 2024-01, 2025-01, 2025-06; 25 rows each, sorted by
-accession). The sample exists only to let reviewers inspect data quality without
-materializing the full ~50 GB corpus; it is *not* a recommended train/eval split.
+Not a sample — exhaustive over the date range.
 
 **Who was involved in the data collection?**
-The authors.
+The authors and engineering team within the Stanford Advanced Financial Technologies Lab.
 
 **Over what timeframe was the data collected?**
-Filings cover 2022-01 through 2025-06. Collection (download + parse) was performed in 2025–2026.
+Filings cover 2022-01 through 2025-06. Collection (download + parse) was performed in 2024–2026 on shared HPC infrastructure.
 
 **Were any ethical review processes conducted?**
 The data is publicly filed regulatory disclosures, so no IRB review was required. The authors confirmed compliance with SEC's terms of service and data-access guidance.
@@ -133,7 +129,7 @@ The SFD parser source will be released alongside paper acceptance. See paper §3
 ## Distribution
 
 **Will the dataset be distributed to third parties?**
-Yes — publicly via HuggingFace Datasets at <https://huggingface.co/datasets/anonymous-md/EDGAR_FILINGS_DATASET>.
+Yes — publicly via HuggingFace Datasets at <https://huggingface.co/datasets/mikedd/EDGAR_FILINGS_DATASET>.
 
 **How will the dataset be distributed?**
 Parquet shards on HuggingFace, accessible via `datasets.load_dataset(...)`.
@@ -154,10 +150,10 @@ No.
 ## Maintenance
 
 **Who is supporting/maintaining the dataset?**
-The Anonymous Authors.
+The Stanford Advanced Financial Technologies Lab.
 
 **How can the owner/curator be contacted?**
-Anonymous during double-blind review. HuggingFace repo discussions for issue reports.
+Email: `nbetts@g.ucla.edu` (Nick Bettencourt) or `giesecke@stanford.edu` (Kay Giesecke). HuggingFace repo discussions for issue reports.
 
 **Is there an erratum?**
 None at v1 release; future errata will be appended to this datasheet and tagged in the HF repo.