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Standardize Electric Sheep Africa dataset card

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  1. README.md +139 -76
README.md CHANGED
@@ -5,93 +5,115 @@ language:
5
  task_categories:
6
  - tabular-classification
7
  - tabular-regression
8
- multilinguality: monolingual
9
  size_categories:
10
  - n<1K
11
  tags:
12
- - tabular
13
- - xlsx
14
- - africa
15
- - chad
16
- - official-statistics
17
- - open-data
18
- - ict
 
 
 
 
 
 
 
19
  configs:
20
  - config_name: default
21
  data_files:
22
  - split: train
23
  path: data/train-00000-of-00001.parquet
24
- pretty_name: "Explosive Weapons Monitor Data by Insecurity Insight | Africa (Chad official open data)"
25
  ---
26
 
27
- # Explosive Weapons Monitor Data by Insecurity Insight | Africa (Chad official open data)
28
 
29
- 332 rows - 1 Africa country - 2018-2025 - Repackaged by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)
30
 
31
  ![rows](https://img.shields.io/badge/rows-332-blue)
32
  ![countries](https://img.shields.io/badge/countries-1-green)
33
- ![years](https://img.shields.io/badge/years-2018-2025-orange)
34
  ![indicators](https://img.shields.io/badge/indicators-0-purple)
35
- ![license](https://img.shields.io/badge/license-cc-by-sa-4.0-lightgrey)
36
 
37
  ## TL;DR
38
 
39
- This dataset packages one official `XLSX` resource from **Chad** as
40
- ML-ready Parquet. The source file is the provenance boundary; all usable
41
- indicators or tabular columns from the resource stay together in this repo.
42
 
43
- ## About the source
44
 
45
- - **Source:** [Explosive Weapons Monitor Data by Insecurity Insight](https://data.humdata.org/dataset/explosive-weapons-use-affecting-aid-access-education-and-healthcare-services)
46
- - **Publisher:** Insecurity Insight
47
- - **Resource:** [2018-2024 EWIPA and Water Data.xlsx](https://data.humdata.org/dataset/729040e4-e253-44ca-b265-123d452738d6/resource/6db3f6ae-81a6-4315-b49e-243d19fa1baf/download/2018-2024-ewipa-and-water-data.xlsx)
48
- - **Format:** `XLSX`
49
- - **License:** [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/)
50
- - **Packaging mode:** `tabular_resource`
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
51
 
52
- ## Geographic coverage
53
 
54
- 1 Africa country:
55
 
56
- | Country | Rows | First year | Last year | Name |
57
- |---------|-----:|-----------:|----------:|------|
58
  | `TCD` | 332 | 2018 | 2025 | `Chad` |
59
 
60
- ## Indicators or Resource Contents
61
 
62
- - This source file is packaged as a normalized tabular resource.
63
 
64
  ## Schema
65
 
66
  | Column | Type | Description | Example |
67
  |--------|------|-------------|---------|
68
- | `source_record_id` | `string` | Stable row identifier for tabular resources. | `6db3f6ae-81a6-4315-b49e-243d19fa1baf:2018-2024-ewipa-and-water-data:0` |
69
- | `country_iso3` | `category` | ISO3 country code. | `TCD` |
70
- | `country_name` | `category` | Country name. | `Chad` |
71
- | `source_sheet` | `string` | Workbook sheet name, when the source is a spreadsheet. | `2018-2024 EWIPA and Water Data` |
72
  | `date` | `string` | Observation date. | `2018-01-05 00:00:00` |
73
- | `country` | `string` | Source column. | `Yemen` |
74
- | `country_iso` | `string` | Source column. | `YEM` |
75
- | `latitude` | `float64` | Source column. | `17.0183` |
76
- | `longitude` | `float64` | Source column. | `43.6819` |
77
- | `location` | `string` | Source column. | `At Talh` |
78
- | `geo_precision` | `string` | Source column. | `(2) 25 km precision` |
79
- | `reported_perpetrator` | `string` | Source column. | `Other` |
80
- | `reported_perpetrator_name` | `string` | Source column. | `International Coalition Forces in Syria` |
81
- | `weapon_carried_used` | `string` | Source column. | `Aerial Bomb: Plane` |
82
- | `event_id` | `float64` | Source column. | `99575.0` |
83
- | `source_period_start_year` | `Int64` | First year inferred from source resource metadata. | `2018` |
84
- | `source_period_end_year` | `Int64` | Last year inferred from source resource metadata. | `2025` |
85
- | `source_period_label` | `category` | Human-readable period inferred from source resource metadata. | `2018-2025` |
86
- | `source_provider` | `category` | Publishing organization. | `Insecurity Insight` |
87
- | `source_dataset` | `category` | Source package title. | `Explosive Weapons Monitor Data by Insecurity Insight` |
88
- | `source_resource` | `category` | Source resource title. | `2018-2024 EWIPA and Water Data.xlsx` |
89
- | `source_package_id` | `category` | CKAN package UUID. | `729040e4-e253-44ca-b265-123d452738d6` |
90
- | `source_resource_id` | `category` | CKAN resource UUID. | `6db3f6ae-81a6-4315-b49e-243d19fa1baf` |
91
- | `source_url` | `category` | Original source resource URL. | `https://data.humdata.org/dataset/729040e4-e253-44ca-b265-123d452738d6/re` |
92
- | `license_id` | `category` | Source license identifier. | `cc-by-sa` |
93
- | `retrieved_at` | `category` | UTC retrieval timestamp. | `2026-08-10T22:51:43Z` |
94
- | `citation` | `string` | Source column. | `` |
95
 
96
  ## Usage
97
 
@@ -103,51 +125,92 @@ df = ds["train"].to_pandas()
103
  print(df.head())
104
  ```
105
 
106
- ### Filter to one country
107
 
108
  ```python
109
- sample_country = df[df["country_iso3"] == "TCD"]
 
110
  ```
111
 
112
- ### Work with indicators
113
 
114
  ```python
115
- if "indicator_id" in df.columns:
116
- print(df["indicator_id"].value_counts().head())
117
- sample = df.sort_values([c for c in ["indicator_id", "year"] if c in df.columns])
118
  ```
119
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
120
  ## Citation
121
 
122
  ```bibtex
123
  @misc{electric_sheep_africa_africa_chad_explosive_weapons_monitor_data_by_insecurity_insight_2a205f32_2025,
124
- title = {Explosive Weapons Monitor Data by Insecurity Insight | Africa (Chad official open data)},
125
  author = {Insecurity Insight},
126
  year = {2025},
127
  url = {https://data.humdata.org/dataset/explosive-weapons-use-affecting-aid-access-education-and-healthcare-services},
128
- publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
129
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-chad-explosive-weapons-monitor-data-by-insecurity-insight-2a205f32}}
130
  }
131
  ```
132
 
133
  ## License
134
 
135
- Released under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/).
136
-
137
- Original data (c) Insecurity Insight. When using this dataset, please cite both the
138
- original source above and the Electric Sheep Africa repackaging.
139
 
140
- ## About Electric Sheep
 
 
141
 
142
- Electric Sheep Africa is part of the Electric Sheep mission: a unified,
143
- ML-ready data layer for Africa on Hugging Face. We pull data from authoritative
144
- open sources, normalize the schemas, package as Parquet, and publish with
145
- consistent dataset cards so researchers and developers can use `load_dataset()`
146
- to start working in seconds.
147
 
148
- Browse the full collection: [huggingface.co/electricsheepafrica](https://huggingface.co/electricsheepafrica)
149
 
150
  ---
151
 
152
- Provenance: ingested 2026-08-10 via the Electric Sheep pipeline. Source URL:
153
- https://data.humdata.org/dataset/729040e4-e253-44ca-b265-123d452738d6/resource/6db3f6ae-81a6-4315-b49e-243d19fa1baf/download/2018-2024-ewipa-and-water-data.xlsx
 
5
  task_categories:
6
  - tabular-classification
7
  - tabular-regression
8
+ multilinguality: multilingual
9
  size_categories:
10
  - n<1K
11
  tags:
12
+ - "tabular"
13
+ - "africa"
14
+ - "open-data"
15
+ - "official-statistics"
16
+ - "chad"
17
+ - "insecurity-insight"
18
+ - "afg"
19
+ - "ago"
20
+ - "arm"
21
+ - "aze"
22
+ - "bgd"
23
+ - "bel"
24
+ - "bol"
25
+ - "bra"
26
  configs:
27
  - config_name: default
28
  data_files:
29
  - split: train
30
  path: data/train-00000-of-00001.parquet
31
+ pretty_name: "Explosive Weapons Monitor Data by Insecurity Insight | Africa (Insecurity Insight)"
32
  ---
33
 
34
+ # Explosive Weapons Monitor Data by Insecurity Insight | Africa (Insecurity Insight)
35
 
36
+ **332 rows** - **1 Africa country/area** - **2018-2025** - **source table** - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*
37
 
38
  ![rows](https://img.shields.io/badge/rows-332-blue)
39
  ![countries](https://img.shields.io/badge/countries-1-green)
40
+ ![period](https://img.shields.io/badge/period-2018--2025-orange)
41
  ![indicators](https://img.shields.io/badge/indicators-0-purple)
42
+ ![license](https://img.shields.io/badge/license-cc--by--sa--4.0-lightgrey)
43
 
44
  ## TL;DR
45
 
46
+ This dataset contains **332 rows** from **Insecurity Insight**, covering **Explosive Weapons Monitor Data by Insecurity Insight**. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.
 
 
47
 
48
+ ## What This Dataset Measures
49
 
50
+ Official statistics datasets help analysts inspect public data as published by governments, national statistical systems, and regional data portals.
51
+
52
+ Source-provided context: Dataset covering 01 January 2018 to 25 April 2025 on incidents in which water systems were impacted by [explosive weapons](https://insecurityinsight.org/projects/explosive-weapons) based on agency- and open source events. Categorised by country.
53
+
54
+ ## How To Read This Dataset
55
+
56
+ - **One row means:** one source record from the original tabular resource, with Electric Sheep Africa provenance columns added where available.
57
+ - **Primary geography column:** `country_iso3`.
58
+ - **Best time column:** `not detected`.
59
+ - **Time coverage basis:** source metadata.
60
+ - **Recommended join keys:** `country_iso3` where available plus source-specific keys.
61
+
62
+ ## Coverage
63
+
64
+ | Dimension | Value |
65
+ |---|---:|
66
+ | Rows | 332 |
67
+ | Countries/areas | 1 |
68
+ | First period | 2018 |
69
+ | Last period | 2025 |
70
+ | Indicators | 0 |
71
+ | Columns | 27 |
72
+ | Source format | XLSX |
73
 
74
+ ## Geographic Coverage
75
 
76
+ Top areas shown below, sorted by row count when available:
77
 
78
+ | Area | Rows | First year | Last year | Name |
79
+ |------|-----:|-----------:|----------:|------|
80
  | `TCD` | 332 | 2018 | 2025 | `Chad` |
81
 
82
+ ## Indicators, Variables, Or Resource Contents
83
 
84
+ - This repo preserves one source tabular resource with its usable columns kept together.
85
 
86
  ## Schema
87
 
88
  | Column | Type | Description | Example |
89
  |--------|------|-------------|---------|
90
+ | `source_record_id` | `string` | Stable row identifier assigned during Electric Sheep Africa engineering. | `6db3f6ae-81a6-4315-b49e-243d19fa1baf:2018-2024-ewipa-and-water-data:0` |
91
+ | `country_iso3` | `dictionary<values=string, indices=int8, ordered=0>` | ISO3 country or area code. | `TCD` |
92
+ | `country_name` | `dictionary<values=string, indices=int8, ordered=0>` | Country or area name. | `Chad` |
93
+ | `source_sheet` | `string` | Source column from the original resource. | `2018-2024 EWIPA and Water Data` |
94
  | `date` | `string` | Observation date. | `2018-01-05 00:00:00` |
95
+ | `country` | `string` | Source column from the original resource. | `Yemen` |
96
+ | `country_iso` | `string` | Source column from the original resource. | `YEM` |
97
+ | `latitude` | `double` | Source column from the original resource. | `17.0183` |
98
+ | `longitude` | `double` | Source column from the original resource. | `43.6819` |
99
+ | `location` | `string` | Source column from the original resource. | `At Talh` |
100
+ | `geo_precision` | `string` | Source column from the original resource. | `(2) 25 km precision` |
101
+ | `reported_perpetrator` | `string` | Source column from the original resource. | `Other` |
102
+ | `reported_perpetrator_name` | `string` | Source column from the original resource. | `International Coalition Forces in Syria` |
103
+ | `weapon_carried_used` | `string` | Source column from the original resource. | `Aerial Bomb: Plane` |
104
+ | `event_id` | `double` | Source column from the original resource. | `99575.0` |
105
+ | `source_period_start_year` | `int64` | Start year inferred from source metadata. | `2018` |
106
+ | `source_period_end_year` | `int64` | End year inferred from source metadata. | `2025` |
107
+ | `source_period_label` | `dictionary<values=string, indices=int8, ordered=0>` | Source column from the original resource. | `2018-2025` |
108
+ | `source_provider` | `dictionary<values=string, indices=int8, ordered=0>` | Publishing organization. | `Insecurity Insight` |
109
+ | `source_dataset` | `dictionary<values=string, indices=int8, ordered=0>` | Source dataset or package title. | `Explosive Weapons Monitor Data by Insecurity Insight` |
110
+ | `source_resource` | `dictionary<values=string, indices=int8, ordered=0>` | Source resource title, table name, or file name. | `2018-2024 EWIPA and Water Data.xlsx` |
111
+ | `source_package_id` | `dictionary<values=string, indices=int8, ordered=0>` | Source package identifier. | `729040e4-e253-44ca-b265-123d452738d6` |
112
+ | `source_resource_id` | `dictionary<values=string, indices=int8, ordered=0>` | Source resource identifier. | `6db3f6ae-81a6-4315-b49e-243d19fa1baf` |
113
+ | `source_url` | `dictionary<values=string, indices=int8, ordered=0>` | Original source URL or download URL. | `https://data.humdata.org/dataset/729040e4-e253-44ca-b265-123d452738d6...` |
114
+ | `license_id` | `dictionary<values=string, indices=int8, ordered=0>` | Source license identifier. | `cc-by-sa` |
115
+ | `retrieved_at` | `dictionary<values=string, indices=int8, ordered=0>` | UTC source retrieval timestamp from the Electric Sheep Africa pipeline. | `2026-08-10T22:51:43Z` |
116
+ | `citation` | `string` | Source column from the original resource. | `` |
117
 
118
  ## Usage
119
 
 
125
  print(df.head())
126
  ```
127
 
128
+ ### Inspect Columns
129
 
130
  ```python
131
+ print(df.info())
132
+ print(df.head())
133
  ```
134
 
135
+ ### Filter By Geography
136
 
137
  ```python
138
+ if "country_iso3" in df.columns:
139
+ sample = df[df["country_iso3"] == "TCD"]
 
140
  ```
141
 
142
+ ### Time-Series Pattern
143
+
144
+ ```python
145
+ if "value" in df.columns and "year" in df.columns:
146
+ trend = df.sort_values("year")
147
+ ```
148
+
149
+ ### Pivot For Analysis
150
+
151
+ ```python
152
+ if {"indicator_id", "year", "value"}.issubset(df.columns):
153
+ matrix = df.pivot_table(index="year", columns="indicator_id", values="value")
154
+ print(matrix.tail())
155
+ ```
156
+
157
+ ## Data Quality Notes
158
+
159
+ - No canonical year/date column was detected in the packaged table; use source metadata and domain context for temporal interpretation.
160
+ - Missing values are preserved rather than silently imputed.
161
+ - Column names are standardized for machine use; source meanings are preserved where known.
162
+ - Always confirm source methodology, units, and collection definitions before policy, production, or redistribution-sensitive use.
163
+
164
+ ## Source And Provenance
165
+
166
+ - **Source:** [Insecurity Insight](https://data.humdata.org/dataset/explosive-weapons-use-affecting-aid-access-education-and-healthcare-services)
167
+ - **Publisher:** Insecurity Insight
168
+ - **Portal:** [https://data.humdata.org](https://data.humdata.org)
169
+ - **Resource:** [2018-2024 EWIPA and Water Data.xlsx](https://data.humdata.org/dataset/729040e4-e253-44ca-b265-123d452738d6/resource/6db3f6ae-81a6-4315-b49e-243d19fa1baf/download/2018-2024-ewipa-and-water-data.xlsx)
170
+ - **License:** [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)
171
+ - **Retrieved/generated:** `2026-08-10T22:51:54Z`
172
+ - **Hugging Face repo:** [electricsheepafrica/africa-chad-explosive-weapons-monitor-data-by-insecurity-insight-2a205f32](https://huggingface.co/datasets/electricsheepafrica/africa-chad-explosive-weapons-monitor-data-by-insecurity-insight-2a205f32)
173
+
174
+ ## Transformations Applied
175
+
176
+ - Converted the source table to Parquet for efficient analytics and ML workflows.
177
+ - Added or preserved source provenance columns where available.
178
+ - Standardized README metadata, dataset loading configuration, schema documentation, and citation format.
179
+ - Preserved source-reported values without analytical imputation.
180
+
181
+ ## Suggested Analyses
182
+
183
+ - Profile the distribution of values
184
+ - Compare categories or geographies
185
+ - Join with complementary public datasets
186
+ - Check missingness before modeling
187
+ - Use `country_iso3` as the safest geography join key when present
188
+
189
  ## Citation
190
 
191
  ```bibtex
192
  @misc{electric_sheep_africa_africa_chad_explosive_weapons_monitor_data_by_insecurity_insight_2a205f32_2025,
193
+ title = {Explosive Weapons Monitor Data by Insecurity Insight | Africa (Insecurity Insight)},
194
  author = {Insecurity Insight},
195
  year = {2025},
196
  url = {https://data.humdata.org/dataset/explosive-weapons-use-affecting-aid-access-education-and-healthcare-services},
197
+ publisher = {Hugging Face Datasets, engineered by Electric Sheep Africa},
198
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-chad-explosive-weapons-monitor-data-by-insecurity-insight-2a205f32}}
199
  }
200
  ```
201
 
202
  ## License
203
 
204
+ Released under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/).
 
 
 
205
 
206
+ Original data is published by Insecurity Insight. Electric Sheep Africa
207
+ engineering standardizes the data for discovery, loading, and analysis on
208
+ Hugging Face. Cite both the original source and this ML-ready dataset when used.
209
 
210
+ ## About Electric Sheep Africa
 
 
 
 
211
 
212
+ Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.
213
 
214
  ---
215
 
216
+ Provenance: README standardized 2026-08-11 by the Electric Sheep Africa README system. Source URL: https://data.humdata.org/dataset/explosive-weapons-use-affecting-aid-access-education-and-healthcare-services