Datasets:

FSTM-LIM
/

bolar

---
task_categories:
- feature-extraction
language:
- ar
size_categories:
- 100K<n<1M
pretty_name: Moroccan Land-Registration Notices (Bulletin Officiel, 2020–2026)
tags:
  - information-extraction
  - structured-extraction
  - arabic
  - legal
  - land-administration
  - low-resource
  - gold-standard
---
# Moroccan Land-Registration Notices (Bulletin Officiel, 2020–2026)

208,833 Arabic land-registration notices published in the *Bulletin Officiel* of
the Kingdom of Morocco between 2020 and 2026, with structured fields extracted
for each, plus a 198-notice evaluation set annotated by hand and independently
twice.

Under Article 37 of the Dahir of 12 August 1913, as amended by Law 14-07, an
application to register a parcel must be published in the *Bulletin Officiel*,
and any third party has two months from that publication to file an objection.
The published extract is therefore not an administrative summary but the legal
instrument that opens the objection window. A century of this record exists only
as Arabic text inside PDF files; none of it was queryable before this work.

## ⚠️ Read this before using the `full` config

**The two configurations do not have the same quality and must never be pooled.**

| config | notices | fields produced by | use for |
|---|---:|---|---|
| `full` | 208,833 | a deterministic parser | deriving, mining, pre-training, weak supervision |
| `gold` | 198 × 2 | human annotators, twice, independently | **evaluation only** |

The `full` config is *silver* data. Measured against the `gold` config, its
per-field F1 has a median of 96.4 % — but that median hides a wide spread. Four
fields are exact and 24 of 29 exceed 90 %, while the administrative levels are
much weaker:

| field | F1 (%) | field | F1 (%) |
|---|---:|---|---:|
| `bo_origine`, `bo_origine_date` | 100.0 | `situation.prefecture` | 87.7 |
| `indice`, `requisitions_concernees` | 100.0 | `droits_reels` | 87.0 |
| `concerne_requisition` | 99.3 | `zone_collective` | 85.7 |
| `demandeurs.nom` | 98.4 | `situation.quartier` | 79.1 |
| `date_depot` | 98.1 | **`situation.commune`** | **73.4** |

A dataset in which dates are right 98 % of the time but communes are right 73 %
of the time supports some queries and quietly breaks others. Check the field you
depend on before you depend on it.

**Do not evaluate a system on the `full` config.** Its labels come from the
system this dataset was built to evaluate. Evaluating on them measures agreement
with that parser, not correctness.

## Quick start

```python
from datasets import load_dataset
full = load_dataset("<org>/moroccan-land-registration-notices", "full", split="train")
gold = load_dataset("<org>/moroccan-land-registration-notices", "gold", split="adjudicated")
# gold has three splits: annotator_1, annotator_2 (both unadjudicated),
# and adjudicated — the one to score against.
print(full[0]["source_text"])      # texte arabe brut de l'annonce
print(full[0]["situation"])         # {"texte": ..., "prefecture": ..., "commune": ...}
```

The `id` column joins the two configs: all 198 `gold` ids are present in `full`,
so the parser's output and the human annotation for the same notice can be
compared directly.

```python
gold_ids = set(gold["id"])
paired = full.filter(lambda r: r["id"] in gold_ids)   # 198 notices
```

## What is in the corpus

336 bulletin issues, 2020–2026, 83 registry offices, two editorial sections.

| notice type | count | section |
|---|---:|---|
| مطلب التحفيظ — registration application | 98,220 | A |
| مطلب التحفيظ — demarcation completion | 102,891 | B |
| خلاصة إصلاحية — rectifying summary | 6,314 | A, B |
| إصلاح غلط — erratum | 1,245 | A, B |
| التحفيظ الجماعي — collective registration | 163 | B |

Counts are by *effective* type. Note the trap in row one and two: the upstream
header does not distinguish a registration application from a
demarcation-completion notice — **the `section` column does**. Section A carries
the initial extracts, section B the demarcation-completion notices. 2,226
notices are published under a header that contradicts their content and are
reclassified by textual markers; `notice_type` keeps the header, and
`notice_type_effective` the reclassification.

Source text is 127 MB, median 541 characters per notice. It comes from the PDF
text layer via `pdftotext`; there is no OCR stage, so its defects are
character-order and layout artefacts, not recognition errors.

## Fields

Identification and provenance:

| column | type | note |
|---|---|---|
| `id` | string | `{bulletin}_{section}_{requisition}`, unique |
| `bulletin`, `year`, `publication_date` | string, int16, string | issue of the gazette |
| `section` | string | `A` or `B` — see the trap above |
| `registry_office` | string | one of 83 |
| `requisition_number`, `notice_type`, `notice_type_effective` | string | |
| `page_start`, `page_end` | int32 | page range in the PDF |
| `source_text` | string | the verbatim Arabic notice — the input of the task |
| `annotation_source` | string | `parser` in `full`, `human` in `gold` |
| `annotator` | string | non-null in `gold` only |

Extracted fields — 29 in the evaluation schema, all normalised rather than
span-based (dates are ISO, areas are square metres, references are canonical,
shares are fractions), which is why value-level scoring is the only meaningful
protocol here:

| column | type |
|---|---|
| `date_depot`, `date_bornage`, `date_bornage_prevue` | string (ISO) |
| `demandeurs` | list of `{nom, part}` |
| `nom_donne`, `nom_actuel`, `nom_propriete`, `nature` | string |
| `situation` | struct `{texte, prefecture, cercle, caidat, commune, douar, quartier}` |
| `superficie_m2`, `superficie_texte` | float64, string |
| `limites` | struct `{nord, est, sud, ouest}`, each `{texte, ref_titres[], ref_requisitions[]}` |
| `droits_reels` | string |
| `origine_propriete` | list of `{texte, type, date}` |
| `bo_origine`, `bo_origine_date`, `concerne_propriete`, `concerne_requisition` | string |
| `annule_annonce_bo`, `nouvel_avis` | string, bool |
| `zone_collective`, `indice`, `requisitions_concernees` | string, string, list |
| `parser_slices` | string (JSON) — the parser's intermediate slices, `full` only |

Empty values are `null` throughout. The parser writes `""` and annotators
sometimes wrote `null`; both mean "field absent" and are normalised to `null`
here, because distinguishing them in the published data would invite false
comparisons.

### The cross-reference fields

`limites.*.ref_titres` and `limites.*.ref_requisitions` are the most valuable
and the most difficult part of the dataset. Boundary descriptions mix literal
text ("a river", "a public road", a neighbour's name) with references to *other
parcels*, cited by registration or title number. Resolved across the corpus,
they link parcels to one another — a parcel adjacency structure recovered from
public text alone, in a country whose cadastre publishes no geometries.

References are normalised to a single canonical form from at least five observed
spellings. Where a neighbour is cited without its registry-office code, the
prefix is inherited from the citing requisition, which is sound because a parcel
can only adjoin parcels administered by the same office. **Ambiguous cases are
discarded rather than resolved arbitrarily**: fabricating an identifier would
silently create an adjacency that does not exist, and a false edge is
indistinguishable from a true one downstream.

## The `gold` config

198 notices stratified by type and year, covering 149 bulletin issues and 70
registry offices: 116 registration applications, 50 rectifying summaries, 24
errata, 8 collective registrations.

Each notice was annotated twice and independently. The `gold` config ships
**three splits**: `annotator_1` and `annotator_2` are the two independent
annotations, unadjudicated, and `adjudicated` resolves the 178 values the two
disagree on. Inter-annotator agreement — computed from the two unadjudicated
splits — is a median per-field F1 of 92.3 % (mean 91.1 %). The fields that stay
low are those where the convention is genuinely under-specified rather than
merely unwritten: `situation.quartier` (66.7 %), `situation.commune` (80.2 %),
`origine_propriete.type` (82.4 %).

**How this gold standard was built matters, and we recommend reading the
accompanying paper before trusting it.** The first version was produced by
having an annotator correct pre-filled parser output — the economical and
widespread protocol. Against that version the parser scored 100 % precision on
22 of 29 fields. Re-annotating the same notices under an instruction to
*recompute rather than validate* dropped the same fields to between 36 and 67 %.
The published version is the re-annotated one. One caveat survives:

Shipping the two unadjudicated splits is deliberate: a user may adopt either
annotator, take their intersection, or re-adjudicate under their own conventions,
and the residual disagreement stays visible instead of being absorbed into a
merge. The **`adjudicated`** split is the single reference for those who need one.
It was resolved from the source text alone — never against the parser, whose
labels a reference adjudicated against it would agree with for reasons no score
would reveal — and it is the split the benchmark is scored against. If you report
one number on this dataset, report it on `adjudicated`.

The annotation guide is included in the repository as `ANNOTATION_GUIDE.md`. It
is what makes the agreement figures interpretable, and the incompleteness of its
first version is the documented cause of part of the initial disagreement.

## Personal and sensitive information

**This dataset names people.** It contains 265,031 mentions of natural persons —
**163,356 distinct names** — each associated with a declared share in an
identified property at an identified location. Names appear both in the
`demandeurs` field and, for 96 % of them, verbatim inside `source_text`.

Redacting the names is not an option that preserves the dataset: `source_text`
is the input of the extraction task, and a censored input measures nothing.
Pseudonymising only the `demandeurs` field would be worse than useless — it would
suggest a protection that the raw text immediately defeats.

The publication in the gazette is constitutive, not incidental: it is what opens
the objection window, and the record is legally required to be public. That is a
real argument, and it is not a sufficient one. Publishing a gazette as PDF and
publishing a structured, indexable, forkable corpus are different acts with
different consequences, and we do not treat the first as authorising the second
without conditions. Hence:

- access is **gated**, under the conditions stated on the access form;
- users undertake not to re-identify, enrich, or cross-reference individuals;
- records are **withdrawn on request** — open an issue or contact the maintainer;
- no derived aggregate over named individuals is published here.

If you are building on this dataset, the honest default is to work from the
non-personal fields (location, area, dates, boundaries, references) and to touch
`demandeurs` and `source_text` only when your task genuinely requires them.

## Known limitations

- **Party names are surface forms.** Homonymy and orthographic variation are not
  resolved, and the same person may appear under several spellings. Person-level
  entity resolution is future work; do not treat a name as an identifier.
- **The `full` labels are parser output** with the per-field quality given
  above, not ground truth.
- **The parser is partly tuned to the `gold` set.** Ten corrections were guided
  by error analysis on it, so `full`'s quality on unseen notices may be slightly
  below the reported figures.
- **The evaluation set is 0.09 % of the corpus**, stratified by type and year
  but not by registry office — 70 offices for 198 notices.
- **Coverage is 2020–2026 only.** The gazette has published these notices for
  over a century; the earlier record is not included.
- **Bidirectional text.** Identifiers mixing Arabic letters and digits are
  stored in *logical* order, which does not match how they render. Reading the
  rendering rather than the string reverses them. Both of our annotators made
  this error, in opposite directions. Compare strings, not screenshots.

## Licensing

Two distinct layers, and they do not have the same status:

- **The annotations, the extracted fields, the schema and the tooling** are
  released by the dataset authors under CC BY 4.0.
- **The source text** is an official publication of the Kingdom of Morocco. It
  is reproduced here as short extracts of a legal instrument for research
  purposes. Its reuse is governed by the terms of the *Bulletin Officiel* and
  not by the licence above.

Use of the dataset is additionally subject to the access conditions.

## Contact and withdrawal

To request the withdrawal of a record, or to report an error in an extracted
field, open an issue on this repository or contact the maintainer. Withdrawal
requests concerning personal data are honoured without justification.

	---
	task_categories:
	- feature-extraction
	language:
	- ar
	size_categories:
	- 100K<n<1M
	pretty_name: Moroccan Land-Registration Notices (Bulletin Officiel, 2020–2026)
	tags:
	- information-extraction
	- structured-extraction
	- arabic
	- legal
	- land-administration
	- low-resource
	- gold-standard
	---

	# Moroccan Land-Registration Notices (Bulletin Officiel, 2020–2026)

	208,833 Arabic land-registration notices published in the Bulletin Officiel of
	the Kingdom of Morocco between 2020 and 2026, with structured fields extracted
	for each, plus a 198-notice evaluation set annotated by hand and independently
	twice.

	Under Article 37 of the Dahir of 12 August 1913, as amended by Law 14-07, an
	application to register a parcel must be published in the Bulletin Officiel,
	and any third party has two months from that publication to file an objection.
	The published extract is therefore not an administrative summary but the legal
	instrument that opens the objection window. A century of this record exists only
	as Arabic text inside PDF files; none of it was queryable before this work.

	## ⚠️ Read this before using the `full` config

	The two configurations do not have the same quality and must never be pooled.

	\| config \| notices \| fields produced by \| use for \|
	\|---\|---:\|---\|---\|
	\| `full` \| 208,833 \| a deterministic parser \| deriving, mining, pre-training, weak supervision \|
	\| `gold` \| 198 × 2 \| human annotators, twice, independently \| evaluation only \|

	The `full` config is silver data. Measured against the `gold` config, its
	per-field F1 has a median of 96.4 % — but that median hides a wide spread. Four
	fields are exact and 24 of 29 exceed 90 %, while the administrative levels are
	much weaker:

	\| field \| F1 (%) \| field \| F1 (%) \|
	\|---\|---:\|---\|---:\|
	\| `bo_origine`, `bo_origine_date` \| 100.0 \| `situation.prefecture` \| 87.7 \|
	\| `indice`, `requisitions_concernees` \| 100.0 \| `droits_reels` \| 87.0 \|
	\| `concerne_requisition` \| 99.3 \| `zone_collective` \| 85.7 \|
	\| `demandeurs.nom` \| 98.4 \| `situation.quartier` \| 79.1 \|
	\| `date_depot` \| 98.1 \| `situation.commune` \| 73.4 \|

	A dataset in which dates are right 98 % of the time but communes are right 73 %
	of the time supports some queries and quietly breaks others. Check the field you
	depend on before you depend on it.

	Do not evaluate a system on the `full` config. Its labels come from the
	system this dataset was built to evaluate. Evaluating on them measures agreement
	with that parser, not correctness.

	## Quick start

	```python
	from datasets import load_dataset

	full = load_dataset("<org>/moroccan-land-registration-notices", "full", split="train")
	gold = load_dataset("<org>/moroccan-land-registration-notices", "gold", split="adjudicated")
	# gold has three splits: annotator_1, annotator_2 (both unadjudicated),
	# and adjudicated — the one to score against.

	print(full[0]["source_text"]) # texte arabe brut de l'annonce
	print(full[0]["situation"]) # {"texte": ..., "prefecture": ..., "commune": ...}
	```

	The `id` column joins the two configs: all 198 `gold` ids are present in `full`,
	so the parser's output and the human annotation for the same notice can be
	compared directly.

	```python
	gold_ids = set(gold["id"])
	paired = full.filter(lambda r: r["id"] in gold_ids) # 198 notices
	```

	## What is in the corpus

	336 bulletin issues, 2020–2026, 83 registry offices, two editorial sections.

	\| notice type \| count \| section \|
	\|---\|---:\|---\|
	\| مطلب التحفيظ — registration application \| 98,220 \| A \|
	\| مطلب التحفيظ — demarcation completion \| 102,891 \| B \|
	\| خلاصة إصلاحية — rectifying summary \| 6,314 \| A, B \|
	\| إصلاح غلط — erratum \| 1,245 \| A, B \|
	\| التحفيظ الجماعي — collective registration \| 163 \| B \|

	Counts are by effective type. Note the trap in row one and two: the upstream
	header does not distinguish a registration application from a
	demarcation-completion notice — the `section` column does. Section A carries
	the initial extracts, section B the demarcation-completion notices. 2,226
	notices are published under a header that contradicts their content and are
	reclassified by textual markers; `notice_type` keeps the header, and
	`notice_type_effective` the reclassification.

	Source text is 127 MB, median 541 characters per notice. It comes from the PDF
	text layer via `pdftotext`; there is no OCR stage, so its defects are
	character-order and layout artefacts, not recognition errors.

	## Fields

	Identification and provenance:

	\| column \| type \| note \|
	\|---\|---\|---\|
	\| `id` \| string \| `{bulletin}_{section}_{requisition}`, unique \|
	\| `bulletin`, `year`, `publication_date` \| string, int16, string \| issue of the gazette \|
	\| `section` \| string \| `A` or `B` — see the trap above \|
	\| `registry_office` \| string \| one of 83 \|
	\| `requisition_number`, `notice_type`, `notice_type_effective` \| string \| \|
	\| `page_start`, `page_end` \| int32 \| page range in the PDF \|
	\| `source_text` \| string \| the verbatim Arabic notice — the input of the task \|
	\| `annotation_source` \| string \| `parser` in `full`, `human` in `gold` \|
	\| `annotator` \| string \| non-null in `gold` only \|

	Extracted fields — 29 in the evaluation schema, all normalised rather than
	span-based (dates are ISO, areas are square metres, references are canonical,
	shares are fractions), which is why value-level scoring is the only meaningful
	protocol here:

	\| column \| type \|
	\|---\|---\|
	\| `date_depot`, `date_bornage`, `date_bornage_prevue` \| string (ISO) \|
	\| `demandeurs` \| list of `{nom, part}` \|
	\| `nom_donne`, `nom_actuel`, `nom_propriete`, `nature` \| string \|
	\| `situation` \| struct `{texte, prefecture, cercle, caidat, commune, douar, quartier}` \|
	\| `superficie_m2`, `superficie_texte` \| float64, string \|
	\| `limites` \| struct `{nord, est, sud, ouest}`, each `{texte, ref_titres[], ref_requisitions[]}` \|
	\| `droits_reels` \| string \|
	\| `origine_propriete` \| list of `{texte, type, date}` \|
	\| `bo_origine`, `bo_origine_date`, `concerne_propriete`, `concerne_requisition` \| string \|
	\| `annule_annonce_bo`, `nouvel_avis` \| string, bool \|
	\| `zone_collective`, `indice`, `requisitions_concernees` \| string, string, list \|
	\| `parser_slices` \| string (JSON) — the parser's intermediate slices, `full` only \|

	Empty values are `null` throughout. The parser writes `""` and annotators
	sometimes wrote `null`; both mean "field absent" and are normalised to `null`
	here, because distinguishing them in the published data would invite false
	comparisons.

	### The cross-reference fields

	`limites..ref_titres` and `limites..ref_requisitions` are the most valuable
	and the most difficult part of the dataset. Boundary descriptions mix literal
	text ("a river", "a public road", a neighbour's name) with references to *other
	parcels*, cited by registration or title number. Resolved across the corpus,
	they link parcels to one another — a parcel adjacency structure recovered from
	public text alone, in a country whose cadastre publishes no geometries.

	References are normalised to a single canonical form from at least five observed
	spellings. Where a neighbour is cited without its registry-office code, the
	prefix is inherited from the citing requisition, which is sound because a parcel
	can only adjoin parcels administered by the same office. **Ambiguous cases are
	discarded rather than resolved arbitrarily**: fabricating an identifier would
	silently create an adjacency that does not exist, and a false edge is
	indistinguishable from a true one downstream.

	## The `gold` config

	198 notices stratified by type and year, covering 149 bulletin issues and 70
	registry offices: 116 registration applications, 50 rectifying summaries, 24
	errata, 8 collective registrations.

	Each notice was annotated twice and independently. The `gold` config ships
	three splits: `annotator_1` and `annotator_2` are the two independent
	annotations, unadjudicated, and `adjudicated` resolves the 178 values the two
	disagree on. Inter-annotator agreement — computed from the two unadjudicated
	splits — is a median per-field F1 of 92.3 % (mean 91.1 %). The fields that stay
	low are those where the convention is genuinely under-specified rather than
	merely unwritten: `situation.quartier` (66.7 %), `situation.commune` (80.2 %),
	`origine_propriete.type` (82.4 %).

	**How this gold standard was built matters, and we recommend reading the
	accompanying paper before trusting it.** The first version was produced by
	having an annotator correct pre-filled parser output — the economical and
	widespread protocol. Against that version the parser scored 100 % precision on
	22 of 29 fields. Re-annotating the same notices under an instruction to
	recompute rather than validate dropped the same fields to between 36 and 67 %.
	The published version is the re-annotated one. One caveat survives:

	Shipping the two unadjudicated splits is deliberate: a user may adopt either
	annotator, take their intersection, or re-adjudicate under their own conventions,
	and the residual disagreement stays visible instead of being absorbed into a
	merge. The `adjudicated` split is the single reference for those who need one.
	It was resolved from the source text alone — never against the parser, whose
	labels a reference adjudicated against it would agree with for reasons no score
	would reveal — and it is the split the benchmark is scored against. If you report
	one number on this dataset, report it on `adjudicated`.

	The annotation guide is included in the repository as `ANNOTATION_GUIDE.md`. It
	is what makes the agreement figures interpretable, and the incompleteness of its
	first version is the documented cause of part of the initial disagreement.

	## Personal and sensitive information

	This dataset names people. It contains 265,031 mentions of natural persons —
	163,356 distinct names — each associated with a declared share in an
	identified property at an identified location. Names appear both in the
	`demandeurs` field and, for 96 % of them, verbatim inside `source_text`.

	Redacting the names is not an option that preserves the dataset: `source_text`
	is the input of the extraction task, and a censored input measures nothing.
	Pseudonymising only the `demandeurs` field would be worse than useless — it would
	suggest a protection that the raw text immediately defeats.

	The publication in the gazette is constitutive, not incidental: it is what opens
	the objection window, and the record is legally required to be public. That is a
	real argument, and it is not a sufficient one. Publishing a gazette as PDF and
	publishing a structured, indexable, forkable corpus are different acts with
	different consequences, and we do not treat the first as authorising the second
	without conditions. Hence:

	- access is gated, under the conditions stated on the access form;
	- users undertake not to re-identify, enrich, or cross-reference individuals;
	- records are withdrawn on request — open an issue or contact the maintainer;
	- no derived aggregate over named individuals is published here.

	If you are building on this dataset, the honest default is to work from the
	non-personal fields (location, area, dates, boundaries, references) and to touch
	`demandeurs` and `source_text` only when your task genuinely requires them.

	## Known limitations

	- Party names are surface forms. Homonymy and orthographic variation are not
	resolved, and the same person may appear under several spellings. Person-level
	entity resolution is future work; do not treat a name as an identifier.
	- The `full` labels are parser output with the per-field quality given
	above, not ground truth.
	- The parser is partly tuned to the `gold` set. Ten corrections were guided
	by error analysis on it, so `full`'s quality on unseen notices may be slightly
	below the reported figures.
	- The evaluation set is 0.09 % of the corpus, stratified by type and year
	but not by registry office — 70 offices for 198 notices.
	- Coverage is 2020–2026 only. The gazette has published these notices for
	over a century; the earlier record is not included.
	- Bidirectional text. Identifiers mixing Arabic letters and digits are
	stored in logical order, which does not match how they render. Reading the
	rendering rather than the string reverses them. Both of our annotators made
	this error, in opposite directions. Compare strings, not screenshots.

	## Licensing

	Two distinct layers, and they do not have the same status:

	- The annotations, the extracted fields, the schema and the tooling are
	released by the dataset authors under CC BY 4.0.
	- The source text is an official publication of the Kingdom of Morocco. It
	is reproduced here as short extracts of a legal instrument for research
	purposes. Its reuse is governed by the terms of the Bulletin Officiel and
	not by the licence above.

	Use of the dataset is additionally subject to the access conditions.

	## Contact and withdrawal

	To request the withdrawal of a record, or to report an error in an extracted
	field, open an issue on this repository or contact the maintainer. Withdrawal
	requests concerning personal data are honoured without justification.