Evidence map›Paper›PMID 37810026›Full record

ReviewHeliyon2023

The evolution of data pricing: From economics to computational intelligence.

Jun Hao, Zeyu Deng, Jianping Li

Abstract readReview
In one paragraph

Review in Heliyon, 2023. 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

3 authors.

Jun HaoSchool of Economics and Management, University of Chinese Academy of Sciences, Beijing, China.
Zeyu DengSchool of Economics and Management, University of Chinese Academy of Sciences, Beijing, China.
Jianping LiSchool of Economics and Management, University of Chinese Academy of Sciences, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Data pricing, which aids in articulating the worth and advantages of data and encourages its opening, sharing, and circulation, is an indispensable component of data trading. Studies pertinent to the topic of data pricing are continuously developing. To undertake a thorough analysis of the literature in data pricing, we use bibliometric and statistical methods for the first time. The 373 data pricing publications from 1990 to 2023 are the study's research object. We mainly analyze the external characteristics, keyword co-occurrence and co-citation networks of data pricing. We find that data pricing has been progressing fairly quickly during the last decade. Two basic approaches have been used by researchers to study how to price various data objects: one is based on economics theory, and the other on computer science algorithms. We provide an in-depth study of the overall evolution of data pricing and give the future directions of data pricing.

Indexed as

Bibliometric analysisData marketData pricingFuture workPricing approach

Identifiers

PMID37810026
PMCPMC10560029

What OpenQuestion holds

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LicenceCC BY
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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.