Indonesian pharmaceutical dataset for self-medication.

Richard Wiputra, Carrie Florista Benjaminsz, Andrian Loria, Rafaell Widjaya, Rudy, Andry Chowanda

Abstract read

In one paragraph

Article in Data in brief, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Richard WiputraInformation Systems Department, School of Information Systems, Bina Nusantara University, Jakarta, Indonesia.

Carrie Florista BenjaminszComputer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia.

Andrian LoriaComputer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia.

Rafaell WidjayaComputer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia.

RudyInformation Systems Department, School of Information Systems, Bina Nusantara University, Jakarta, Indonesia.

Andry ChowandaComputer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Indonesian Pharmaceutical Dataset for Self-medication consists of two structured datasets containing some of the most important public health information: a drug dataset and a disease dataset. Both were extracted from the websites of Indonesian-registered and regulated telemedicine providers. The drug dataset contains general data on drugs, indications, dosages, side effects, contraindications, and warnings, whereas the disease dataset contains definitions, descriptions, symptoms, and causes of diseases. Both datasets are provided in CSV file format and are available exclusively in Bahasa Indonesia to maintain consistency with the source content and cater to local users' needs. These datasets are available to facilitate research, application development, and Indonesian health information systems through locally contextualized and accessible health data for the Indonesian population to use. Some potential applications include powering health chatbots, arming medical search tools, guiding health literacy programs, and facilitating the integration of standardized local information into HealthTech platforms.

Indexed as

Drug and diseaseIndonesian datasetSelf-medicationWeb scraping

Identifiers

PMID41624302

DOI10.1016/j.dib.2026.112460

PMCPMC12854858

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.