Dominik A. Haas: <i>Gāyatrī: Mantra and Mother of the Vedas</i> . Vienna: Austrian Academy of Sciences Press, 2023. 385 pp. Open access. ISBN 978-3-7001-9390-6

Article Dominik A. Haas: Gāyatrī: Mantra and Mother of the Vedas. Vienna: Austrian Academy of Sciences Press, 2023. 385 pp. Open access. ISBN 978-3-7001-9390-6 was published on May 1, 2026 in the journal Asiatische Studien - Études Asiatiques (volume 80, issue 1).

Authors

Institutions

Publication Details

Journal
Asiatische Studien – Études Asiatiques
Published
2026-05-01
DOI
https://doi.org/10.1515/asia-2026-0001
Primary Topic
Indian and Buddhist Studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Dominik A. Haas: Gāyatrī: Mantra and Mother of the Vedas . Vienna: Austrian Academy of Sciences Press, 2023. 385 pp. Open access. ISBN 978-3-7001-9390-6

Borayin Larios
Asiatische Studien – Études Asiatiques
Indian and Buddhist Studies
article

Dominik A. Haas: Gāyatrī: Mantra and Mother of the Vedas . Vienna: Austrian Academy of Sciences Press, 2023. 385 pp. Open access. ISBN 978-3-7001-9390-6

Borayin Larios
article en

Abstract

Article Dominik A. Haas: Gāyatrī: Mantra and Mother of the Vedas. Vienna: Austrian Academy of Sciences Press, 2023. 385 pp. Open access. ISBN 978-3-7001-9390-6 was published on May 1, 2026 in the journal Asiatische Studien - Études Asiatiques (volume 80, issue 1).

Asiatische Studien – Études AsiatiquesVol. 80(1)
Austrian Centre of Industrial Biotechnology (Austria) (AT)
Openalex Percentile: Top 2%
Indian and Buddhist Studies
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.