Newborns in Kazakhstan dataset
Year: 2026
Type: dataset
Abstract: Dataset Compilation Process The following process was donoe in Python. This dataset contains deifentified anthropometric data of newborns born in medical hospitals of Kazakhstan. Data Acquisition and Merging The dataset was initially compiled by iterating through 912 Excel files downloaded from the open database of the Smart Data Ukimet, it is a national information and analytical platform designed for the collection, processing, storage, and analysis of data from more than 120 government databases. During this phase, metadata such as the year, quarter, and region were extracted from the filenames using regular expressions, such as 2013Q3_Almaty_obl_.... All individual files were then concatenated into a single master DataFrame, which contained approximately 4.05 million records. Feature Selection To streamline the data for analysis, the original 14 columns underwent a feature selection process. Technical and redundant columns—specifically id, outcome_date, med_org_name, sdu_load_in_dt, source_file, region_id, region, and quartile—were removed. The final output focuses exclusively on five core variables: weight, height, gender, birth_date, and region_name, along with a temporary year filter. Temporal Filtering and Data Cleaning Data integrity was ensured by converting the year column to a numeric format and applying a temporal filter. This restricted the dataset to records spanning from 2013 to 2024, effectively excluding any older or unknown records. Throughout the merging process, data types were verified, ensuring physical measurements were stored as integers and categorical data as objects. Any missing values caused by unrecognized filename patterns were simply marked as 'Unknown'. Export Configuration Because the filtered version of the dataset remained quite large, containing approximately 3.4 million rows, it was exported as a CSV file to bypass the 1,048,576 row limit standard to Excel sheets. This final file was saved as newborn_data_5columns.csv and was exported without the DataFrame index to maintain a clean and streamlined structure. https://ashyq.data.gov.kz/dataset/magda-ds-3a875c52-a394-4978-a505-695ec4372056/details?q=%D0%BD%D0%BE%D0%B2%D0%BE%D1%80%D0%BE%D0%B6%D0%B4%D0%B5 https://ashyq.data.gov.kz/dataset/magda-ds-9b95a459-d39b-4d8a-a220-7df65c273bc2/details?q=%D0%BD%D0%BE%D0%B2%D0%BE%D1%80%D0%BE%D0%B6%D0%B4%D0%B5 https://ashyq.data.gov.kz/dataset/magda-ds-72a57b9f-ad57-42e4-9a9c-48bd5ffd1b47/details?q=%D0%BD%D0%BE%D0%B2%D0%BE%D1%80%D0%BE%D0%B6%D0%B4%D0%B5 https://ashyq.data.gov.kz/dataset/magda-ds-be3feec9-e2a1-44c3-aa8a-4f4b8ef64b49/details?q=%D0%BD%D0%BE%D0%B2%D0%BE%D1%80%D0%BE%D0%B6%D0%B4%D0%B5 https://ashyq.data.gov.kz/dataset/magda-ds-2949e4c5-56da-4b86-9649-ed7e41b9997c/details?q=%D0%BD%D0%BE%D0%B2%D0%BE%D1%80%D0%BE%D0%B6%D0%B4%D0%B5 https://ashyq.data.gov.kz/dataset/magda-ds-e76834cb-f82d-437c-bd77-911049a856ee/details?q=%D0%BD%D0%BE%D0%B2%D0%BE%D1%80%D0%BE%D0%B6%D0%B4%D0%B5 https://ashyq.data.gov.kz/dataset/magda-ds-6af1c33f-4a39-4dcf-bf7d-8b911150d95a/details?q=%D0%BD%D0%BE%D0%B2%D0%BE%D1%80%D0%BE%D0%B6%D0%B4%D0%B5 https://ashyq.data.gov.kz/dataset/magda-ds-b45f148c-acdc-4df4-8430-2035755b316d/details?q=%D0%BD%D0%BE%D0%B2%D0%BE%D1%80%D0%BE%D0%B6%D0%B4%D0%B5 https://ashyq.data.gov.kz/dataset/magda-ds-b30d6910-fb77-40be-bfd2-e5d1c4b69a82/details?q=%D0%BD%D0%BE%D0%B2%D0%BE%D1%80%D0%BE%D0%B6%D0%B4%D0%B5 https://ashyq.data.gov.kz/dataset/magda-ds-738d41d4-7be6-41d7-8f0f-128ffdfd6d7a/details?q=%D0%BD%D0%BE%D0%B2%D0%BE%D1%80%D0%BE%D0%B6%D0%B4%D0%B5 https://ashyq.data.gov.kz/dataset/magda-ds-d009900f-5ec7-4795-b131-960406f70390/details?q=%D0%BD%D0%BE%D0%B2%D0%BE%D1%80%D0%BE%D0%B6%D0%B4%D0%B5 https://ashyq.data.gov.kz/dataset/magda-ds-412793cc-3c9f-406b-bfc8-8565c07c46eb/details?q=%D0%BD%D0%BE%D0%B2%D0%BE%D1%80%D0%BE%D0%B6%D0%B4%D0%B5 https://ashyq.data.gov.kz/dataset/magda-ds-09acfb35-71ba-4f83-be20-1044bc83ea8d/details?q=%D0%BD%D0%BE%D0%B2%D0%BE%D1%80%D0%BE%D0%B6%D0%B4%D0%B5 https://ashyq.data.gov.kz/dataset/magda-ds-4a63a0f0-5d5b-41ad-97bb-fe9de2e7e0a3/details?q=%D0%BD%D0%BE%D0%B2%D0%BE%D1%80%D0%BE%D0%B6%D0%B4%D0%B5 https://ashyq.data.gov.kz/dataset/magda-ds-94073e3f-4dd2-4f62-b53b-db632efdb06c/details?q=%D0%BD%D0%BE%D0%B2%D0%BE%D1%80%D0%BE%D0%B6%D0%B4%D0%B5 https://ashyq.data.gov.kz/dataset/magda-ds-a276920b-0b25-44e3-800f-e480f4e609ad/details?q=%D0%BD%D0%BE%D0%B2%D0%BE%D1%80%D0%BE%D0%B6%D0%B4%D0%B5 https://ashyq.data.gov.kz/dataset/magda-ds-886113e3-80fe-47fb-ab0b-22ee89327323/details?q=%D0%BD%D0%BE%D0%B2%D0%BE%D1%80%D0%BE%D0%B6%D0%B4%D0%B5 https://ashyq.data.gov.kz/dataset/magda-ds-d26bd6d0-98c7-4b85-923c-faabdbc28d67/details?q=%D0%BD%D0%BE%D0%B2%D0%BE%D1%80%D0%BE%D0%B6%D0%B4%D0%B5 https://ashyq.data.gov.kz/dataset/magda-ds-26ae9f56-c793-4a02-9327-ef19cb2cea1a/details?q=%D0%BD%D0%BE%D0%B2%D0%BE%D1%80%D0%BE%D0%B6%D0%B4%D0%B5 https://ashyq.data.gov.kz/dataset/magda-ds-18fb3ddb-1fdc-48e7-adc6-e74756d29cec/details?q=%D0%BD%D0%BE%D0%B2%D0%BE%D1%80%D0%BE%D0%B6%D0%B4%D0%B5 https://ashyq.data.gov.kz/dataset/magda-ds-2949e4c5-56da-4b86-9649-ed7e41b9997c/details?q=%D0%B0%D0%BA%D0%BC%D0%BE%D0%BB
Author: Davletov, Dimash
Institution: Kazakh National Medical University
Language: English
Subfield: General Health Professions
Field: Health Professions
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7171432104