Add README.md

README.md

@@ -5,91 +5,50 @@ language_creators:
 - found
 language:
 - en
-license: other
+license: cc-by-4.0
 multilinguality:
 - monolingual
 size_categories:
-- 10K<n<100K
+- n<1K
 source_datasets:
 - original
 task_categories:
 - tabular-classification
 - tabular-regression
+- other
 task_ids: []
 tags:
 - africa
 - humanitarian
 - hdx
 - electric-sheep-africa
+- conflict-violence
 - food-security
-- integrated-food-security-phase-classification-ipc
-- afg
-- ago
-- bdi
-- caf
+- cmr
 - cod
-pretty_name: IPC Acute Food Insecurity Country Data
+- irq
+- lbn
+- mli
+pretty_name: "Conflict-Related Incidents Affecting Water Systems"
 dataset_info:
-  features:
-  - name: date
-    dtype: timestamp[ns]
-  - name: country
-    dtype: string
-  - name: country_iso
-    dtype: string
-  - name: admin_1
-    dtype: string
-  - name: geo_precision
-    dtype: string
-  - name: reported_perpetrator
-    dtype: string
-  - name: reported_perpetrator_name
-    dtype: string
-  - name: weapon_carried_used
-    dtype: string
-  - name: water_infrastructure_category_affected
-    dtype: string
-  - name: number_of_attacks_damaging_destroying_water_infrastructure
-    dtype: float64
-  - name: sind_event_id
-    dtype: int64
-  - name: date_event_entered
-    dtype: timestamp[ns]
-  - name: date_event_modified
-    dtype: timestamp[ns]
-  - name: esa_source
-    dtype: string
-  - name: esa_processed
-    dtype: string
   splits:
-  - name: train
-    num_bytes: 66420
-    num_examples: 340
-  - name: test
-    num_bytes: 16950
-    num_examples: 86
-  download_size: 28050
-  dataset_size: 83370
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+    - name: train
+      num_examples: 340
+    - name: test
+      num_examples: 85
 ---
 
-# IPC Acute Food Insecurity Country Data
+# Conflict-Related Incidents Affecting Water Systems
 
-**Publisher:** Integrated Food Security Phase Classification (IPC) · **Source:** [HDX](https://data.humdata.org/dataset/ipc-country-data) · **License:** `other-pd-nr` · **Updated:** 2025-04-09
+**Publisher:** Insecurity Insight · **Source:** [HDX](https://data.humdata.org/dataset/conflict-related-incidents-affecting-water-systems) · **License:** `cc-by-igo` · **Updated:** 2026-05-04
 
 ---
 
 ## Abstract
 
-The IPC Acute Food Insecurity (IPC AFI) classification provides strategically relevant information to decision makers that focuses on short-term objectives to prevent, mitigate or decrease severe food insecurity that threatens lives or livelihoods. This data has been produced by the National IPC Technical Working Groups for IPC population estimates since 2017. All national population figures are based on official country population estimates. IPC estimates are those published in country IPC reports.
+This page contains data on conflict events with clearly foreseeable impacts on or links to water systems based on agency-and open source events. Categorized by country. Covers Cameroon, DRC, Lebanon, Mali, Myanmar, Niger, oPt, Somalia, Sudan, Syria and Yemen. Please get in touch if you are interested in curated datasets: info@insecurityinsight.org
 
-Each row in this dataset represents tabular records. Temporal coverage is indicated by the `unnamed_5` column(s). Geographic scope: **AFG, AGO, BDI, CAF, COD, DJI, DOM, SLV, and 22 others**.
+Each row in this dataset represents discrete events or incidents. Temporal coverage is indicated by the `date`, `date_event_entered` column(s). Geographic scope: **CMR, COD, IRQ, LBN, MLI, NER, SSD, PSE, and 4 others**.
 
 *Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*
 
@@ -100,20 +59,26 @@ Each row in this dataset represents tabular records. Temporal coverage is indica
 | | |
 |---|---|
 | **Domain** | Food security and nutrition |
-| **Unit of observation** | Tabular records |
-| **Rows (total)** | 10,890 |
-| **Columns** | 38 (29 numeric, 8 categorical, 1 datetime) |
-| **Train split** | 8,712 rows |
-| **Test split** | 2,178 rows |
-| **Geographic scope** | AFG, AGO, BDI, CAF, COD, DJI, DOM, SLV, and 22 others |
-| **Publisher** | Integrated Food Security Phase Classification (IPC) |
-| **HDX last updated** | 2025-04-09 |
+| **Unit of observation** | Discrete events or incidents |
+| **Rows (total)** | 426 |
+| **Columns** | 15 (2 numeric, 10 categorical, 3 datetime) |
+| **Train split** | 340 rows |
+| **Test split** | 85 rows |
+| **Geographic scope** | CMR, COD, IRQ, LBN, MLI, NER, SSD, PSE, and 4 others |
+| **Publisher** | Insecurity Insight |
+| **HDX last updated** | 2026-05-04 |
 
 ---
 
 ## Variables
 
-**Identifier / Metadata** — `unnamed_0` (Somalia, Yemen, Sudan), `unnamed_1` (Upper Nile, Jonglei, Gedo), `unnamed_2` (Beloha, Ambovombe-androy, Bekily), `unnamed_3` (range 12135260.0–33434386.0), `unnamed_4` (Acute Food Insecurity November 2022, Acute Food Insecurity November 2020, Acute Food Insecurity August 2022) and 33 others.
+**Geographic** — `country` (OPT, Syria, Ukraine), `country_iso` (PSE, SYR, UKR), `admin_1` (West Bank, Gaza Strip, South Governorate), `water_infrastructure_category_affected` (Water Distribution, Multi Purpose/Function Infrastructure, Water Storage), `number_of_attacks_damaging_destroying_water_infrastructure` (range 1.0–7.0).
+
+**Temporal** — `date`, `date_event_entered`, `date_event_modified`.
+
+**Identifier / Metadata** — `reported_perpetrator_name` (Israeli Defence Forces, Armed Forces of the Russian Federation, Unidentified Armed Actor), `sind_event_id` (range 4537.0–126962.0), `esa_source` (HDX), `esa_processed` (2026-05-04).
+
+**Other** — `geo_precision` (censored), `reported_perpetrator` (Government: Military, Foreign Forces: Military, NSA), `weapon_carried_used` (Firearms, Aerial Bomb: Plane, Aerial Bomb: Drone).
 
 ---
 
@@ -136,42 +101,19 @@ train.head()
 
 | Column | Type | Null % | Range / Sample Values |
 |---|---|---|---|
-| `unnamed_0` | object | 0.0% | Somalia, Yemen, Sudan |
-| `unnamed_1` | object | 28.1% | Upper Nile, Jonglei, Gedo |
-| `unnamed_2` | object | 13.7% | Beloha, Ambovombe-androy, Bekily |
-| `unnamed_3` | float64 | 13.7% | 12135260.0 – 33434386.0 (mean 19573588.5937) |
-| `unnamed_4` | object | 13.7% | Acute Food Insecurity November 2022, Acute Food Insecurity November 2020, Acute Food Insecurity August 2022 |
-| `unnamed_5` | datetime64[ns] | 0.1% |  |
-| `unnamed_7` | float64 | 11.5% | 219.0 – 102971764.0 (mean 750175.7863) |
-| `unnamed_9` | float64 | 23.5% | 1.0 – 5.0 (mean 2.6614) |
-| `unnamed_10` | object | 11.5% | Oct 2020 - Dec 2020, Jul 2022 - Sep 2022, Oct 2022 - Dec 2022 |
-| `unnamed_11` | float64 | 11.5% | 0.0 – 34950493.0 (mean 297231.5188) |
-| `unnamed_12` | float64 | 11.5% | 0.0 – 1.0 (mean 0.3884) |
-| `unnamed_13` | float64 | 11.5% | 0.0 – 64014318.0 (mean 263124.07) |
-| `unnamed_14` | float64 | 11.5% | 0.0 – 1.0 (mean 0.3254) |
-| `unnamed_15` | float64 | 11.7% | 0.0 – 22597681.0 (mean 144216.7686) |
-| `unnamed_16` | float64 | 11.7% | 0.0 – 0.8 (mean 0.2157) |
-| `unnamed_17` | float64 | 12.8% | 0.0 – 6856155.0 (mean 45035.0342) |
-| `unnamed_18` | float64 | 12.8% | 0.0 – 0.62 (mean 0.07) |
-| `unnamed_19` | float64 | 13.3% | 0.0 – 352896.0 (mean 493.0271) |
-| `unnamed_20` | float64 | 13.3% | 0.0 – 0.3 (mean 0.0011) |
-| `unnamed_21` | float64 | 11.5% | 0.0 – 27262321.0 (mean 188830.0041) |
-| `unnamed_22` | float64 | 11.5% | 0.0 – 0.95 (mean 0.2854) |
-| `unnamed_23` | float64 | 11.8% | 111.0 – 105251041.0 (mean 703443.2123) |
-| `unnamed_25` | float64 | 24.2% | 1.0 – 5.0 (mean 2.7889) |
-| `unnamed_26` | object | 11.7% | Oct 2022 - Dec 2022, Oct 2020 - Dec 2020, Jan 2021 - Jun 2021 |
-| `unnamed_27` | float64 | 12.1% | 0.0 – 31540609.0 (mean 258469.4216) |
-| `unnamed_28` | float64 | 12.1% | 0.0 – 1.0 (mean 0.3537) |
-| `unnamed_29` | float64 | 12.1% | 0.0 – 47830924.0 (mean 247713.6927) |
-| `unnamed_30` | float64 | 12.1% |  |
-| `unnamed_31` | float64 | 12.3% |  |
-| `unnamed_32` | float64 | 12.3% |  |
-| `unnamed_33` | float64 | 13.1% |  |
-| `unnamed_34` | float64 | 13.1% |  |
-| `unnamed_35` | float64 | 13.8% |  |
-| `unnamed_36` | float64 | 13.7% |  |
-| `unnamed_37` | float64 | 12.1% |  |
-| `unnamed_38` | float64 | 12.1% |  |
+| `date` | datetime64[ns] | 0.0% |  |
+| `country` | object | 0.0% | OPT, Syria, Ukraine |
+| `country_iso` | object | 0.0% | PSE, SYR, UKR |
+| `admin_1` | object | 0.0% | West Bank, Gaza Strip, South Governorate |
+| `geo_precision` | object | 0.0% | censored |
+| `reported_perpetrator` | object | 0.0% | Government: Military, Foreign Forces: Military, NSA |
+| `reported_perpetrator_name` | object | 0.0% | Israeli Defence Forces, Armed Forces of the Russian Federation, Unidentified Armed Actor |
+| `weapon_carried_used` | object | 0.0% | Firearms, Aerial Bomb: Plane, Aerial Bomb: Drone |
+| `water_infrastructure_category_affected` | object | 8.9% | Water Distribution, Multi Purpose/Function Infrastructure, Water Storage |
+| `number_of_attacks_damaging_destroying_water_infrastructure` | float64 | 36.4% | 1.0 – 7.0 (mean 1.0554) |
+| `sind_event_id` | int64 | 0.0% | 4537.0 – 126962.0 (mean 105978.0587) |
+| `date_event_entered` | datetime64[ns] | 0.0% |  |
+| `date_event_modified` | datetime64[ns] | 0.0% |  |
 | `esa_source` | object | 0.0% | HDX |
 | `esa_processed` | object | 0.0% | 2026-05-04 |
 
@@ -181,37 +123,24 @@ train.head()
 
 | Column | Min | Max | Mean | Median |
 |---|---|---|---|---|
-| `unnamed_3` | 12135260.0 | 33434386.0 | 19573588.5937 | 19191866.0 |
-| `unnamed_7` | 219.0 | 102971764.0 | 750175.7863 | 189613.0 |
-| `unnamed_9` | 1.0 | 5.0 | 2.6614 | 3.0 |
-| `unnamed_11` | 0.0 | 34950493.0 | 297231.5188 | 65341.0 |
-| `unnamed_12` | 0.0 | 1.0 | 0.3884 | 0.35 |
-| `unnamed_13` | 0.0 | 64014318.0 | 263124.07 | 59745.0 |
-| `unnamed_14` | 0.0 | 1.0 | 0.3254 | 0.3 |
-| `unnamed_15` | 0.0 | 22597681.0 | 144216.7686 | 35690.0 |
-| `unnamed_16` | 0.0 | 0.8 | 0.2157 | 0.2 |
-| `unnamed_17` | 0.0 | 6856155.0 | 45035.0342 | 6982.0 |
-| `unnamed_18` | 0.0 | 0.62 | 0.07 | 0.05 |
-| `unnamed_19` | 0.0 | 352896.0 | 493.0271 | 0.0 |
-| `unnamed_20` | 0.0 | 0.3 | 0.0011 | 0.0 |
-| `unnamed_21` | 0.0 | 27262321.0 | 188830.0041 | 44735.5 |
-| `unnamed_22` | 0.0 | 0.95 | 0.2854 | 0.25 |
+| `number_of_attacks_damaging_destroying_water_infrastructure` | 1.0 | 7.0 | 1.0554 | 1.0 |
+| `sind_event_id` | 4537.0 | 126962.0 | 105978.0587 | 100152.5 |
 
 ---
 
 ## Curation
 
-Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (`N/A`, `null`, `none`, `-`, `unknown`, `no data`, `#N/A`) were unified to `NaN`. 19 column(s) with >80% missing values were removed: `unnamed_6`, `unnamed_8`, `unnamed_24`, `unnamed_39`, `unnamed_40`, `unnamed_41`.... 221 exact duplicate rows were removed. 30 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.
+Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (`N/A`, `null`, `none`, `-`, `unknown`, `no data`, `#N/A`) were unified to `NaN`. 13 column(s) with >80% missing values were removed: `event_description`, `latitude`, `longitude`, `number_of_attacks_where_water_infrastructure_was_contaminated`, `number_of_attacks_where_water_infrastructure_was_looted`, `number_of_attacks_where_water_infrastructure_was_obstructed`.... The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.
 
 ---
 
 ## Limitations
 
-- Data originates from Integrated Food Security Phase Classification (IPC) and has not been independently validated by ESA.
+- Data originates from Insecurity Insight and has not been independently validated by ESA.
 - Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
-- The following columns have >20% missing values and should be treated with caution in modelling: `unnamed_1`, `unnamed_9`, `unnamed_25`.
-- This dataset spans 30 countries; geographic and methodological inconsistencies across national boundaries may affect cross-country comparability.
-- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/ipc-country-data) for the publisher's own methodology notes and caveats.
+- The following columns have >20% missing values and should be treated with caution in modelling: `number_of_attacks_damaging_destroying_water_infrastructure`.
+- This dataset spans 12 countries; geographic and methodological inconsistencies across national boundaries may affect cross-country comparability.
+- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/conflict-related-incidents-affecting-water-systems) for the publisher's own methodology notes and caveats.
 
 ---
 
@@ -219,10 +148,10 @@ Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Colu
 
 ```bibtex
 @dataset{hdx_asia_food_security_all,
-  title     = {IPC Acute Food Insecurity Country Data},
-  author    = {Integrated Food Security Phase Classification (IPC)},
-  year      = {2025},
-  url       = {https://data.humdata.org/dataset/ipc-country-data},
+  title     = {Conflict-Related Incidents Affecting Water Systems},
+  author    = {Insecurity Insight},
+  year      = {2026},
+  url       = {https://data.humdata.org/dataset/conflict-related-incidents-affecting-water-systems},
   note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
 }
 ```