Datasetpublic

electricsheepasia/asia-aid-flows-financial-tracking-private-sector-nepal2

Financial tracking of private sector contributions Nepal 2015 Publisher: OCHA HQ · Source: HDX · License: cc-by-igo · Updated: 2023-05-02 Abstract Information on the private sector cash and in-kind contributions to humanitarian relief efforts in Nepal earthquake. Each row in this dataset represents tabular records. Temporal coverage is indicated by the unnamed_11, unnamed_12 column(s). Geographic scope: NPL, NEPAL-EARTHQUAKE. Curated into ML-ready Parquet format… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepasia/asia-aid-flows-financial-tracking-private-sector-nepal2.

sourceHugging Facecc-by-4.0updated 5mo agoView on Hugging Face

0likes24downloads

Dataset Card

Financial tracking of private sector contributions Nepal 2015

Publisher: OCHA HQ · Source: HDX · License: cc-by-igo · Updated: 2023-05-02

Abstract

Information on the private sector cash and in-kind contributions to humanitarian relief efforts in Nepal earthquake.

Each row in this dataset represents tabular records. Temporal coverage is indicated by the unnamed_11, unnamed_12 column(s). Geographic scope: NPL, NEPAL-EARTHQUAKE.

Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).

Dataset Characteristics


Domain	Humanitarian and development data
Unit of observation	Tabular records
Rows (total)	229
Columns	20 (2 numeric, 15 categorical, 3 datetime)
Train split	183 rows
Test split	45 rows
Geographic scope	NPL, NEPAL-EARTHQUAKE
Publisher	OCHA HQ
HDX last updated	2023-05-02

Variables

Identifier / Metadata — unnamed_2 (American Red Cross, Unspecified, Canadian Red Cross), unnamed_3 (USD, Amount (converted) Currency), unnamed_4 (range 0.0–4000000.0), unnamed_5 (USD, CAD, EUR), unnamed_6 (range 0.0–20000000.0) and 13 others.

Other — organisation (Dow Chemical Company, Ford Foundation, Medtronic Plc), un_ocha_private_sector_section (Financials Services, Consumer, Health).

Quick Start

python

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-aid-flows-financial-tracking-private-sector-nepal2")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

Schema

Column	Type	Null %	Range / Sample Values
`organisation`	object	0.4%	Dow Chemical Company, Ford Foundation, Medtronic Plc
`un_ocha_private_sector_section`	object	3.1%	Financials Services, Consumer, Health
`unnamed_2`	object	23.1%	American Red Cross, Unspecified, Canadian Red Cross
`unnamed_3`	object	3.9%	USD, Amount (converted) Currency
`unnamed_4`	float64	27.1%	0.0 – 4000000.0 (mean 247213.3749)
`unnamed_5`	object	3.9%	USD, CAD, EUR
`unnamed_6`	float64	27.1%	0.0 – 20000000.0 (mean 746069.238)
`unnamed_7`	object	3.9%	Cash, In-Kind, Cash/In-Kind
`unnamed_8`	object	3.9%	Paid, Pledged, Status
`unnamed_9`	object	3.9%	United States, United Kingdom, Canada
`unnamed_10`	object	3.9%	The four big American carriers ? Verizon, Sprint, AT&T and T-Mobile ? have all confirmed that they won?t be charging customers for calls or SMS messages to Nepal. The companies are also publicizing a variety of text-to-donate options for their customers, initiatives like the one that raised millions of dollars for the American Red Cross after the 2010 earthquake in Haiti., Description, Through Change for Good, the Cathay Pacific inflight fundraising programme, HK$2.6mn was collected from passengers on both Cathay Pacific and Dragonair flights.
`unnamed_11`	datetime64[ns]	4.4%
`unnamed_12`	datetime64[ns]	4.4%
`unnamed_13`	object	3.9%	http://www.uschamberfoundation.org/corporate-citizenship-center/nepal-earthquake-corporate-aid-tracker, http://www.gulf-times.com/qatar/178/details/445633/-cathay-pacific-quake-aid-for-nepal-tops-hk$10mn, http://www.csrwire.com/press_releases/37962-Medtronic-Responds-to-Nepal-Earthquake
`unnamed_14`	object	3.9%
`unnamed_15`	object	7.0%
`unnamed_16`	object	3.9%
`unnamed_17`	datetime64[ns]	4.4%
`esa_source`	object	0.0%
`esa_processed`	object	0.0%

Numeric Summary

Column	Min	Max	Mean	Median
`unnamed_4`	0.0	4000000.0	247213.3749	74962.52
`unnamed_6`	0.0	20000000.0	746069.238	100000.0

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. 2 exact duplicate rows were removed. 5 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.

Limitations

—Data originates from OCHA HQ 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_2, unnamed_4, unnamed_6.
—This dataset spans 2 countries; geographic and methodological inconsistencies across national boundaries may affect cross-country comparability.
—Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

Citation

bibtex

@dataset{hdx_asia_aid_flows_financial_tracking_private_sector_nepal2,
  title     = {Financial tracking of private sector contributions Nepal 2015},
  author    = {OCHA HQ},
  year      = {2023},
  url       = {https://data.humdata.org/dataset/financial-tracking-private-sector-nepal2015},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.