General information

Organisation type
University
ROR
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Research metrics

The calculation window differs from metric to metric: some cover the reporting year, others the whole history of the organisation. Which is which is set out in “Where these numbers come from” below. Recalculation is scheduled, once a day at night.

No data for 11 metrics Average KZ-SJR, Publications, Citations, Average Impact Factor, h-index and 6 more Show how they are calculated Collapse the list

These values have not been calculated for this university yet. How each metric is calculated and what data it needs:

  • Average KZ-SJR

    Average KZ-SJR across the university's journals; used in the university ranking.

    Methodology : Average KZ-SJR

  • Publications

    Number of the university's publications in the reporting year — articles with at least one author affiliated with the university in that year. Each article counts once; removed records and articles whose publication year is only estimated are excluded. An absolute count: it is not divided by staff size.

    Methodology : Publications

  • Citations

    Total citations received by all of the university's publications over the period.

    Methodology : Citations

  • Average Impact Factor

    Average Impact Factor across the university's publications.

    Methodology : Average Impact Factor

  • h-index

    The largest number h such that h publications have each been cited at least h times.

    Methodology : h-index

  • Q1 share

    Share of the university's publications that appear in Q1 journals of their subject subfield.

    Methodology : Q1 share

  • International co-authorship

    Share of publications with at least one foreign-affiliated co-author (P_intl ÷ P_total × 100). Source: Crossref/OpenAlex affiliations.

    Methodology : International co-authorship

  • Inter-university collaboration

    Share of the university's publications for the reporting year that have a co-author affiliated that same year with another Kazakhstani university. Advisory indicator: it does not enter the ranking's final score.

    Methodology : Inter-university collaboration

  • Partner universities

    Number of distinct other Kazakhstani universities that co-authored with the institution in the reporting year's publications — the breadth of collaboration. Advisory indicator: it does not enter the ranking's final score.

    Methodology : Partner universities

  • Citations per researcher

    Total citations to the university's articles divided by its staff headcount (citations per researcher). Computed only when the administrator-entered staff count is known; universities without it are not ranked on this lens rather than scored as zero.

    Methodology : Citations per researcher

  • Grant activity

    Composite of grant activity: funding volume (weight 0.5), grants won (0.3), active grants (0.2). Each component is normalised across all organisations; a component left blank is dropped and the remaining weights are renormalised. An entered zero is data and does take part in the calculation.

    Methodology : Grant activity

Grant activity

No data

Methodology

Where these numbers come from

Not a single number on this page was entered by hand at random. Each one is collected from external sources, matched by identifiers, verified and computed by a fixed formula. An organisation brings a difficulty a journal does not: the same affiliation is written a dozen different ways across sources. Below is the whole path, including what we do not know and where we get it wrong.

Sources

A university’s metrics are collected from external databases and from data the university itself submits. Each source is enabled separately and has its own harvesting schedule:

  • OpenAlex — publications, citations, affiliations
  • Crossref — article metadata and reference lists
  • ORCID — researchers and their achievements
  • ROR — the registry of organisations
  • University-supplied data — staff headcount and grants

Some details do not come from harvesting: editorial offices fill in individual fields, administrators enter and correct data, and file upload is supported. Such records go through the same matching and confidence rules, but do not count as an external source.

Calculation window

The h-index covers the organisation’s entire history; publications, citations, the Q1 share, international co-authorship and grant activity cover the calculation year. The scheduled recomputation runs once a day during the night hours, and public values do not change between runs.

Verification

Affiliations are matched by the organisation identifier and, where there is none, by the normalised name; duplicate organisations are merged rather than discarded. Every record gets a confidence score from 0 to 100, and records below the threshold do not enter the metric calculation.

Manual corrections

Staff headcount and grant components are entered by the university’s administrator. The platform’s editors may correct a computed value when the source data is plainly wrong; such a value is always flagged, and the flag is shown next to it.

The data path

Five steps from someone else’s record of an article to the number on this page. Step two is where organisations go wrong most often.

  1. 1

    Harvesting. Records of articles, references, authors and organisations are pulled from external sources on a schedule. The source of every record is stored with it.

  2. 2

    Resolving affiliations. An affiliation string is matched to an organisation by identifier and, where there is none, by the normalised name in three languages. Unresolved strings are queued rather than attributed to a similar organisation.

  3. 3

    Normalisation. Subjects, author names and organisation names are mapped onto the reference lists. The Kazakh, Russian and English forms of a name are linked to one another.

  4. 4

    Scoring. Every record gets a confidence score from 0 to 100 based on field completeness, agreement between sources and the age of the record. Below the threshold a record does not enter the calculation.

  5. 5

    Computation. The metrics are normalised across the cohort of universities and combined into a score using the weights in the table below. The result is published together with its calculation window, sources and confidence.

Weights of the final score

The same thing that opens under “How it is calculated” on each metric, gathered into one table. The weights are fixed: recomputing with new weights counts as a new edition of the methodology and is dated. A metric that could not be computed for an organisation is dropped along with its weight and the rest are renormalised — it is not replaced by a zero.

Metrics behind the final score: what exactly is counted and the weight
Metric What exactly is counted Weight
Average KZ-SJR The mean KZ-SJR of the journals the university’s articles appeared in, weighted by the number of articles 22
h-index The largest h for which there are h publications cited at least h times each 18
Q1 share The share of articles published in first-quartile journals of their field in the year of publication 13
Citations References to the organisation’s articles, excluding those coming from retracted works 12
Publications Articles carrying the organisation’s affiliation, after duplicates are removed 8
Average Impact Factor The mean impact factor of the journals the articles appeared in, weighted by the number of articles 8
Citations per researcher Citations divided by the staff headcount; without a headcount it is not computed 8
Grant activity Funding 0.5, grants won 0.3, active grants 0.2, with renormalisation 6
International co-authorship The share of articles whose affiliations include an organisation outside Kazakhstan 5

The data quality behind every number

Confidence is a service score of our data about an object, not an expert judgement of the object itself. A low value means the sources disagree with each other, mandatory fields or identifiers are missing, or the data came from a low-trust source.

The platform reconciles differences between sources regularly: critical fields and the metrics that form the ranking at least once a week, less sensitive reference data at least once a month. A small discrepancy is only logged; a significant one creates a manual review task with an assigned owner.

You can report an error in the data through the feedback form: the request enters the tracking system and is followed through to a reply to the person who sent it.

Full methodology Report a data issue

The methodology changes by versions: each has an effective date, and earlier values are never rewritten retroactively.

Specialization areas

Specialization: Not yet confirmed

Based on 3 classified publications

Engineering

1 publication Confidence: 100 out of 100

Correct the name

Business, Management and Accounting

1 publication Confidence: 77 out of 100

Correct the name

Social Sciences

1 publication Confidence: 100 out of 100

Correct the name

Leading researchers

Researchers the organisation’s administrator marked as recommended come first; the rest follow by h-index. Achievements are collected from ORCID and OpenAlex, each with its own confidence score.

  • Omar Isam Al Mrayat

    h-index: 0 ORCID

    Achievements

    • h-index: 3 OpenAlex Confidence: 60 out of 100
    • i10-index: 0 OpenAlex Confidence: 60 out of 100
    • 2-year mean citedness: 0.38 OpenAlex Confidence: 60 out of 100
    • Total citations: 16 OpenAlex Confidence: 60 out of 100
    • Works count: 35 OpenAlex Confidence: 60 out of 100

    Key publications

    • Explainable CNN–BiLSTM Framework for Multi-Class Sleep Apnea Severity Detection Using Single-Lead ECG Signals: A Comprehensive Machine Learning Approach Confidence: 60 out of 100
    • Optimizing Data Migration and Classification Accuracy in Parallel Hybrid Storage Systems Using Efficient Data Mining Techniques Confidence: 60 out of 100
    • An enhanced framework for securing software using iterative 4-D base security cycle appreciative inquiry Confidence: 60 out of 100
    • Integrating User Relationships and Features for Intelligence of Social Things Aware Information Diffusion Prediction Confidence: 60 out of 100
    • AI-Driven Threat Detection Models: Integrating Deep Learning for Enhanced Cybersecurity Against Emerging Network Vulnerabilities Confidence: 60 out of 100
    • Data-Driven Approaches for Crop Yield Prediction Using Machine Learning Techniques Cited 4 times Confidence: 60 out of 100
    • Integrating User Relationships and Features for Intelligence of Social Things Aware Information Diffusion Prediction Cited 3 times Confidence: 60 out of 100
    • AI-Driven Threat Detection Models: Integrating Deep Learning for Enhanced Cybersecurity Against Emerging Network Vulnerabilities Cited 2 times Confidence: 60 out of 100
    • An Extended UTAUT Model to Explain Factors Influencing Smart City Technology Adoption Cited 2 times Confidence: 60 out of 100
    • Appreciative Inquiry in Eliciting Software Requirements Cited 3 times Confidence: 60 out of 100

Partners for international projects

Universities with a similar research profile, ranked with a bonus for cross-border collaboration. Advisory only.

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International ranking crosswalk

This is RankScience data arranged in the structure of the international ranking — not official ranking scores.

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