DOSM's Advance Release Calendar
Data as of 27 Apr 2024, 08:00Release date and metadata for all DOSM's publications in 2024.
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How is this data produced?
DOSM's advance release calendar is compiled and coordinated by the Strategic Communications Division for the purpose of display on the main DOSM portal. It contains three fields, namely the title in English, title in Malay, and release date. To produce the structured dataset shown here, the OpenDOSM team conducts standardisation of naming conventions and adds several metadata fields which are typically helpful for users, such as category, frequency, and release series (in date and natural language formats).
What caveats I should bear in mind when using this data?
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Publication(s) using this data
The data shown here tallies exactly with the Upcoming Publications calendar on OpenDOSM.
Metadata
Dataset description
Release date and metadata for all DOSM's publications in 2024.
Variable definitions
- Publication ID
- Release Date
- Publication Title (EN)
- Title Publication (MS)
- Publication Category
- Publication Category (EN)
- Publication Category (MS)
- Frequency of data
- Series
- Series (Formatted Date)
- Series (EN)
- Series (MS)
Name in Dataset | Variable | Definition |
|---|---|---|
publication_id (String) | Publication ID | Unique ID identifying the publication; this should be the primary key if used in a database. |
release_date (Timestamp) | Release Date | Date and time of release in YYYY-MM-DD HH:MM:SS format. The publication is embargoed until such time. |
title_en (String) | Publication Title (EN) | Title of the publication in English, in the format {title}: {data up to} |
title_bm (String) | Title Publication (MS) | Title of the publication in Malay, in the format {title}: {data up to} |
publication_type (String) | Publication Category | Category of the publication indicating its broad domain. For example, all publications relating to GDP and satellite accounts are categorised as 'gdp'. |
publication_type_en (String) | Publication Category (EN) | Category of the publication in English |
publication_type_bm (String) | Publication Category (MS) | Category of the publication in Malay |
frequency (Categorical) | Frequency of data | Either YEARLY, QUARTERLY, or MONTHLY. Note that this is the frequency of the data series within the publication, NOT the frequency of the publication itself. |
release_series (Date) | Series | Data as of this date, in YYYY-MM-DD format. The earliest date in the period is chosen, e.g. YYYY-01-01 for annual data and YYYY-04-01 for 2nd quarter data. |
release_series_date (String) | Series (Formatted Date) | The 'release_series' field formatted according to frequency. For instance, 2023-01-01 is formatted as 2023 for yearly data, 2023-q1 for quarterly data, and 2023-01 for monthly data. |
release_series_en (String) | Series (EN) | The 'release_series' field formatted as natural language in English. For instance, 2023-01-01 is formatted as 2023 for yearly data, Q1 2023 for quarterly data, and Jan 2023 for monthly data. |
release_series_bm (String) | Series (MS) | The 'release_series' field formatted as natural language in Malay. For instance, 2023-01-01 is formatted as 2023 for yearly data, ST1 2023 for quarterly data, and Jan 2023 for monthly data. |
Last updated:
27 Apr 2024, 08:00
Next update:
28 Apr 2024, 08:00
Data source(s)
- DOSM
License
This data is made open under the Creative Commons Attribution 4.0 International License (CC BY 4.0). A copy of the license is available Here.
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Data
Full Dataset (CSV)
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Full Dataset (Parquet)
Recommended for data scientists seeking to work with data via code.
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Code
Connect directly to the data with Python.
# If not already installed, do: pip install pandas fastparquet
import pandas as pd
URL_DATA = 'https://storage.dosm.gov.my/meta/arc.parquet'
df = pd.read_parquet(URL_DATA)
if 'date' in df.columns: df['date'] = pd.to_datetime(df['date'])
print(df)Sample OpenAPI query
import requests
import pprint
url = "https://api.data.gov.my/data-catalogue?id=arc_dosm&limit=3"
response_json = requests.get(url=url).json()
pprint.pprint(response_json)
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