---
_id: '64894'
abstract:
- lang: eng
text: "This dataset contains experimental measurements of the radial dynamic and
quasi-static characteristics of four different types of Rubber-Metal Bushings
(RMBs) used in the suspension system of a passenger car under harmonic displacement
excitation. For each bushing type, 2–3 individual specimens were tested.\r\n \r\nQuasi-static
measurements were performed at a constant excitation frequency of 0.05 Hz with
varying displacement amplitudes. Dynamic measurements were conducted with displacement
amplitudes ranging from 0.04 mm to 0.3 mm and excitation frequencies of 2, 5,
10, ..., up to 100 Hz.\r\n\r\nThe data is structured by bushing type, measurement
mode, amplitude, and frequency, and is provided in *.csv and *.hrm format. It
is intended to support further research in modeling rubber-metal bushings and
parameter identification techniques."
author:
- first_name: Jan
full_name: Schütte, Jan
id: '22109'
last_name: Schütte
orcid: 0000-0001-9025-9742
citation:
ama: 'Schütte J. Experimental Dataset: Force and Displacement Measurements of
Four Rubber-Metal Bushing Types from a Passenger Car under Harmonic Displacement
Excitation. LibreCat University; 2025. doi:10.5281/ZENODO.14851317'
apa: 'Schütte, J. (2025). Experimental Dataset: Force and Displacement Measurements
of Four Rubber-Metal Bushing Types from a Passenger Car under Harmonic Displacement
Excitation. LibreCat University. https://doi.org/10.5281/ZENODO.14851317'
bibtex: '@book{Schütte_2025, title={Experimental Dataset: Force and Displacement
Measurements of Four Rubber-Metal Bushing Types from a Passenger Car under Harmonic
Displacement Excitation}, DOI={10.5281/ZENODO.14851317},
publisher={LibreCat University}, author={Schütte, Jan}, year={2025} }'
chicago: 'Schütte, Jan. Experimental Dataset: Force and Displacement Measurements
of Four Rubber-Metal Bushing Types from a Passenger Car under Harmonic Displacement
Excitation. LibreCat University, 2025. https://doi.org/10.5281/ZENODO.14851317.'
ieee: 'J. Schütte, Experimental Dataset: Force and Displacement Measurements
of Four Rubber-Metal Bushing Types from a Passenger Car under Harmonic Displacement
Excitation. LibreCat University, 2025.'
mla: 'Schütte, Jan. Experimental Dataset: Force and Displacement Measurements
of Four Rubber-Metal Bushing Types from a Passenger Car under Harmonic Displacement
Excitation. LibreCat University, 2025, doi:10.5281/ZENODO.14851317.'
short: 'J. Schütte, Experimental Dataset: Force and Displacement Measurements of
Four Rubber-Metal Bushing Types from a Passenger Car under Harmonic Displacement
Excitation, LibreCat University, 2025.'
date_created: 2026-03-11T10:22:01Z
date_updated: 2026-03-11T10:25:23Z
department:
- _id: '151'
doi: 10.5281/ZENODO.14851317
keyword:
- bushing
- experimental data
- rubber-metal-bushing
- Dataset suspension
publisher: LibreCat University
status: public
title: 'Experimental Dataset: Force and Displacement Measurements of Four Rubber-Metal
Bushing Types from a Passenger Car under Harmonic Displacement Excitation'
type: research_data
user_id: '22109'
year: '2025'
...
---
_id: '56581'
abstract:
- lang: eng
text: 'In recent years, there has been a surge in natural language processing research
focused on low-resource languages (LrLs), underscoring the growing recognition
that LrLs deserve the same attention as high-resource languages (HrLs). This shift
is crucial for ensuring linguistic diversity and inclusivity in the digital age.
Despite Indonesian ranking as the 11th most spoken language globally, it remains
under-resourced in terms of computational tools and datasets. Within the semantic
web domain, Entity Linking (EL) is pivotal, linking textual entity mentions to
their corresponding entries in knowledge bases. This process is foundational for
advanced information extraction tasks, including relation extraction and event
detection. To bolster EL research in Indonesian, we introduce IndEL, the first
benchmark dataset tailored for both general and specific domains. IndEL was manually
curated using Wikidata, adhering to a rigorous set of annotation guidelines. We
used two Named Entity Recognition (NER) benchmark datasets for entity extraction:
NER UI for the general domain and IndQNER for the specific domain. IndQNER focused
on entities from the Indonesian translation of the Quran. IndEL comprises 4765
entities in the general domain and 2453 in the specific domain. Using the GERBIL
framework, we use IndEL to evaluate the performance of various EL systems, such
as Babelfy, DBpedia Spotlight, MAG, OpenTapioca, and WAT. Our further investigation
reveals that within Wikidata, a significant number of NIL entities remain unlinked
due to the limited number of Indonesian labels and the use of acronyms. Especially
in the specific domain, transliteration and translation processes performed to
create the Indonesian translation of the Quran contribute to the presence of entities
in a descriptive form and as synonyms.'
author:
- first_name: Ria Hari
full_name: Gusmita, Ria Hari
id: '71039'
last_name: Gusmita
- first_name: Muhammad Faruq Amiral
full_name: Abshar, Muhammad Faruq Amiral
last_name: Abshar
- first_name: Diego
full_name: Moussallem, Diego
id: '71635'
last_name: Moussallem
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
citation:
ama: 'Gusmita RH, Abshar MFA, Moussallem D, Ngonga Ngomo A-C. IndEL: Indonesian
Entity Linking Benchmark Dataset for General and Specific Domains. In: Lecture
Notes in Computer Science. Springer Nature Switzerland; 2024. doi:10.1007/978-3-031-70239-6_34'
apa: 'Gusmita, R. H., Abshar, M. F. A., Moussallem, D., & Ngonga Ngomo, A.-C.
(2024). IndEL: Indonesian Entity Linking Benchmark Dataset for General and Specific
Domains. In Lecture Notes in Computer Science. The 29th Annual International
Conference on Natural Language & Information Systems (NLDB 2024), Turin, Italy.
Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-70239-6_34'
bibtex: '@inbook{Gusmita_Abshar_Moussallem_Ngonga Ngomo_2024, place={Cham}, title={IndEL:
Indonesian Entity Linking Benchmark Dataset for General and Specific Domains},
DOI={10.1007/978-3-031-70239-6_34},
booktitle={Lecture Notes in Computer Science}, publisher={Springer Nature Switzerland},
author={Gusmita, Ria Hari and Abshar, Muhammad Faruq Amiral and Moussallem, Diego
and Ngonga Ngomo, Axel-Cyrille}, year={2024} }'
chicago: 'Gusmita, Ria Hari, Muhammad Faruq Amiral Abshar, Diego Moussallem, and
Axel-Cyrille Ngonga Ngomo. “IndEL: Indonesian Entity Linking Benchmark Dataset
for General and Specific Domains.” In Lecture Notes in Computer Science.
Cham: Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-70239-6_34.'
ieee: 'R. H. Gusmita, M. F. A. Abshar, D. Moussallem, and A.-C. Ngonga Ngomo, “IndEL:
Indonesian Entity Linking Benchmark Dataset for General and Specific Domains,”
in Lecture Notes in Computer Science, Cham: Springer Nature Switzerland,
2024.'
mla: 'Gusmita, Ria Hari, et al. “IndEL: Indonesian Entity Linking Benchmark Dataset
for General and Specific Domains.” Lecture Notes in Computer Science, Springer
Nature Switzerland, 2024, doi:10.1007/978-3-031-70239-6_34.'
short: 'R.H. Gusmita, M.F.A. Abshar, D. Moussallem, A.-C. Ngonga Ngomo, in: Lecture
Notes in Computer Science, Springer Nature Switzerland, Cham, 2024.'
conference:
end_date: 2024-06-27
location: Turin, Italy
name: The 29th Annual International Conference on Natural Language & Information
Systems (NLDB 2024)
start_date: 2024-06-25
date_created: 2024-10-10T14:29:08Z
date_updated: 2024-10-14T19:22:16Z
doi: 10.1007/978-3-031-70239-6_34
keyword:
- entity linking benchmark dataset
- Indonesian
- general and specific domains
language:
- iso: eng
place: Cham
publication: Lecture Notes in Computer Science
publication_identifier:
isbn:
- '9783031702389'
- '9783031702396'
issn:
- 0302-9743
- 1611-3349
publication_status: published
publisher: Springer Nature Switzerland
related_material:
link:
- relation: confirmation
url: https://link.springer.com/chapter/10.1007/978-3-031-70239-6_34
status: public
title: 'IndEL: Indonesian Entity Linking Benchmark Dataset for General and Specific
Domains'
type: book_chapter
user_id: '71039'
year: '2024'
...
---
_id: '55835'
abstract:
- lang: eng
text: This dataset contains selective MEI encodings of a number of arrangements
of Beethoven’s Opp. 91, 92, and 93. These encodings were prepared in the context
of the Beethoven in the House project, jointly funded by AHRC and DFG from 2020
to 2023. It is a slight update on v1.0.0 in better organizing the release assets.
author:
- first_name: Lisa
full_name: Rosendahl, Lisa
last_name: Rosendahl
citation:
ama: 'Rosendahl L. Beethoven in the House: Selective Encodings of Arrangements
of Beethoven’s Opp. 91, 92, and 93. Zenodo; 2023. doi:10.5281/ZENODO.7875059'
apa: 'Rosendahl, L. (2023). Beethoven in the House: Selective Encodings of Arrangements
of Beethoven’s opp. 91, 92, and 93. Zenodo. https://doi.org/10.5281/ZENODO.7875059'
bibtex: '@book{Rosendahl_2023, title={Beethoven in the House: Selective Encodings
of Arrangements of Beethoven’s opp. 91, 92, and 93.}, DOI={10.5281/ZENODO.7875059},
publisher={Zenodo}, author={Rosendahl, Lisa}, year={2023} }'
chicago: 'Rosendahl, Lisa. Beethoven in the House: Selective Encodings of Arrangements
of Beethoven’s Opp. 91, 92, and 93. Zenodo, 2023. https://doi.org/10.5281/ZENODO.7875059.'
ieee: 'L. Rosendahl, Beethoven in the House: Selective Encodings of Arrangements
of Beethoven’s opp. 91, 92, and 93. Zenodo, 2023.'
mla: 'Rosendahl, Lisa. Beethoven in the House: Selective Encodings of Arrangements
of Beethoven’s Opp. 91, 92, and 93. Zenodo, 2023, doi:10.5281/ZENODO.7875059.'
short: 'L. Rosendahl, Beethoven in the House: Selective Encodings of Arrangements
of Beethoven’s Opp. 91, 92, and 93., Zenodo, 2023.'
date_created: 2024-08-28T11:43:11Z
date_updated: 2024-08-28T14:09:28Z
department:
- _id: '874'
doi: 10.5281/ZENODO.7875059
keyword:
- BitH
- Dataset
- Domestic Music Arrangements
- Ludwig van Beethoven
- MEI
- Music Encoding Initiative
publisher: Zenodo
status: public
title: 'Beethoven in the House: Selective Encodings of Arrangements of Beethoven’s
opp. 91, 92, and 93.'
type: research_data
user_id: '1684'
year: '2023'
...
---
_id: '46572'
abstract:
- lang: eng
text: Indonesian is classified as underrepresented in the Natural Language Processing
(NLP) field, despite being the tenth most spoken language in the world with 198
million speakers. The paucity of datasets is recognized as the main reason for
the slow advancements in NLP research for underrepresented languages. Significant
attempts were made in 2020 to address this drawback for Indonesian. The Indonesian
Natural Language Understanding (IndoNLU) benchmark was introduced alongside IndoBERT
pre-trained language model. The second benchmark, Indonesian Language Evaluation
Montage (IndoLEM), was presented in the same year. These benchmarks support several
tasks, including Named Entity Recognition (NER). However, all NER datasets are
in the public domain and do not contain domain-specific datasets. To alleviate
this drawback, we introduce IndQNER, a manually annotated NER benchmark dataset
in the religious domain that adheres to a meticulously designed annotation guideline.
Since Indonesia has the world’s largest Muslim population, we build the dataset
from the Indonesian translation of the Quran. The dataset includes 2475 named
entities representing 18 different classes. To assess the annotation quality of
IndQNER, we perform experiments with BiLSTM and CRF-based NER, as well as IndoBERT
fine-tuning. The results reveal that the first model outperforms the second model
achieving 0.98 F1 points. This outcome indicates that IndQNER may be an acceptable
evaluation metric for Indonesian NER tasks in the aforementioned domain, widening
the research’s domain range.
author:
- first_name: Ria Hari
full_name: Gusmita, Ria Hari
id: '71039'
last_name: Gusmita
- first_name: Asep Fajar
full_name: Firmansyah, Asep Fajar
id: '76787'
last_name: Firmansyah
- first_name: Diego
full_name: Moussallem, Diego
id: '71635'
last_name: Moussallem
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
citation:
ama: 'Gusmita RH, Firmansyah AF, Moussallem D, Ngonga Ngomo A-C. IndQNER: Named
Entity Recognition Benchmark Dataset from the Indonesian Translation of the Quran.
In: Natural Language Processing and Information Systems. Springer Nature
Switzerland; 2023. doi:10.1007/978-3-031-35320-8_12'
apa: 'Gusmita, R. H., Firmansyah, A. F., Moussallem, D., & Ngonga Ngomo, A.-C.
(2023). IndQNER: Named Entity Recognition Benchmark Dataset from the Indonesian
Translation of the Quran. In Natural Language Processing and Information Systems.
International Conference on Applications of Natural Language to Information Systems
(NLDB) 2023, Derby, UK. Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-35320-8_12'
bibtex: '@inbook{Gusmita_Firmansyah_Moussallem_Ngonga Ngomo_2023, place={Cham},
title={IndQNER: Named Entity Recognition Benchmark Dataset from the Indonesian
Translation of the Quran}, DOI={10.1007/978-3-031-35320-8_12},
booktitle={Natural Language Processing and Information Systems}, publisher={Springer
Nature Switzerland}, author={Gusmita, Ria Hari and Firmansyah, Asep Fajar and
Moussallem, Diego and Ngonga Ngomo, Axel-Cyrille}, year={2023} }'
chicago: 'Gusmita, Ria Hari, Asep Fajar Firmansyah, Diego Moussallem, and Axel-Cyrille
Ngonga Ngomo. “IndQNER: Named Entity Recognition Benchmark Dataset from the Indonesian
Translation of the Quran.” In Natural Language Processing and Information Systems.
Cham: Springer Nature Switzerland, 2023. https://doi.org/10.1007/978-3-031-35320-8_12.'
ieee: 'R. H. Gusmita, A. F. Firmansyah, D. Moussallem, and A.-C. Ngonga Ngomo, “IndQNER:
Named Entity Recognition Benchmark Dataset from the Indonesian Translation of the Quran,”
in Natural Language Processing and Information Systems, Cham: Springer
Nature Switzerland, 2023.'
mla: 'Gusmita, Ria Hari, et al. “IndQNER: Named Entity Recognition Benchmark Dataset
from the Indonesian Translation of the Quran.” Natural Language Processing
and Information Systems, Springer Nature Switzerland, 2023, doi:10.1007/978-3-031-35320-8_12.'
short: 'R.H. Gusmita, A.F. Firmansyah, D. Moussallem, A.-C. Ngonga Ngomo, in: Natural
Language Processing and Information Systems, Springer Nature Switzerland, Cham,
2023.'
conference:
end_date: 2023-06-23
location: Derby, UK
name: International Conference on Applications of Natural Language to Information
Systems (NLDB) 2023
start_date: 2023-06-21
date_created: 2023-08-17T12:41:45Z
date_updated: 2024-11-19T15:41:34Z
department:
- _id: '34'
- _id: '574'
doi: 10.1007/978-3-031-35320-8_12
keyword:
- NER benchmark dataset
- Indonesian
- specific domain
language:
- iso: eng
place: Cham
publication: Natural Language Processing and Information Systems
publication_identifier:
isbn:
- '9783031353192'
- '9783031353208'
issn:
- 0302-9743
- 1611-3349
publication_status: published
publisher: Springer Nature Switzerland
related_material:
link:
- relation: confirmation
url: https://link.springer.com/chapter/10.1007/978-3-031-35320-8_12
status: public
title: 'IndQNER: Named Entity Recognition Benchmark Dataset from the Indonesian Translation
of the Quran'
type: book_chapter
user_id: '71039'
year: '2023'
...