cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/zncp6xz9zw
title: Supplementary Table 1: Cutaneous adverse events associated with MEK-inhibitors for the treatment of vascular anomalies: a multicenter retrospective study

description: Supplementary Table 1: Most common cutaneous adverse events observed in patients with vascular anomalies taking MEK-inhibitors, excluding patients on concurrent therapies.
DOI: 10.17632/zncp6xz9zw.3
title: Supplementary Table 1: Cutaneous adverse events associated with MEK-inhibitors for the treatment of vascular anomalies: a multicenter retrospective study

description: Supplementary Table 1: Most common cutaneous adverse events observed in patients with vascular anomalies taking MEK-inhibitors, excluding patients on concurrent therapies.
DOI: 10.17632/2fv6vf9v2z.2
title: Kothon: A Large-Scale Dataset for Machine Translation of the Chittagonian and Sylheti Dialects into Standard Bangla

description: Chittagonian and Sylheti are two major and complex Bengali dialects spoken by over 24 million Bengali speakers. However, their well-written forms are becoming increasingly rare, putting them at risk of extinction. As these dialects differ significantly from Standard Bangla, they often create communication barriers for non-dialectal speakers. Despite this, very few research efforts have been made to address the issue. Existing resources are limited to small datasets, which are insufficient for effective preservation of dialects. To bridge this gap, this study focuses on the creation and evaluation of large-scale parallel corpora for the Chittagonian-Bangla and Sylheti-Bangla translation. A total of 8,000 Chittagonian and 9,300 Sylheti sentences were collected and annotated by 5 native dialects speaking annotators. Standard Bangla sentences were gathered from open-source resources, novels, and existing datasets, complemented with text scanning of printed books. A custom web-based annotation tool was developed to aid the annotation process. The quality and reliability of the datasets were also ensured through a rigorous validation process involving independent native speakers, who reviewed translations. This dataset serves as a valuable resource for advancing research in Bengali language processing and supporting the creation of intelligent systems that help preserve dialects and promote digital communication.
DOI: 10.15468/dl.yjezpc
title: Occurrence Download

description: A dataset containing 69 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is CHN.28_1",
 "TaxonKey is Delphinium tianshanicum"
 ]
}

 The dataset includes 69 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002252-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/tjstx5rg6k
title: Low-Resource Corpus Indonesian Local Language

description: This study departs from the hypothesis that combining Neural Machine Translation (NMT) with the stemming algorithms ECS, Porter, and Porter Hybrid (ECS) can improve root-word extraction accuracy for Indonesian regional languages. In particular, the hybrid model which integrates ECS’s morphological sensitivity with Porter’s efficiency is expected to deliver the best performance in preserving semantic equivalence across word forms.
The dataset comprises nine text files representing three regional languages (Javanese, Minangkabau, and Sundanese) and three stemming approaches (ECS, Porter, and Porter Hybrid-ECS). Each file contains pairs of source sentences and outputs produced through the NMT → Stemming → NMT pipeline: regional-language text is translated into Indonesian, stemmed, and then projected back into the original regional language. The parallel corpora were aligned at the lexical level and curated to balance token counts, structural variation, and morphological diversity.
Experimental results reveal several prominent findings. ECS excels for languages with complex affixation because it captures local morphological patterns; Porter is computationally lighter but tends to under-stem prefixes/infices typical of regional languages; and Porter Hybrid (ECS) consistently performs best across all three languages, yielding an average accuracy gain of approximately 4–7% over single models. Evaluation uses precision–recall (assessing correctness and coverage of recovered lemmas), cosine similarity (semantic proximity to reference forms), and BLEU score (n-gram similarity to reference).
Interpretively, the data indicate that a hybrid approach blending linguistic insight (ECS) with a general algorithm (Porter) within an NMT-based pipeline produces a more universal and adaptive stemming system for Indonesian regional languages. These findings are relevant for developing cross-regional NLP applications (machine translation, sentiment analysis, lexical normalization), training models for low-resource languages, and improving text preprocessing in translation systems to achieve more accurate morphology-level handling.
DOI: 10.17632/tjstx5rg6k.1
title: Low-Resource Corpus Indonesian Local Language

description: This study departs from the hypothesis that combining Neural Machine Translation (NMT) with the stemming algorithms ECS, Porter, and Porter Hybrid (ECS) can improve root-word extraction accuracy for Indonesian regional languages. In particular, the hybrid model which integrates ECS’s morphological sensitivity with Porter’s efficiency is expected to deliver the best performance in preserving semantic equivalence across word forms.
The dataset comprises nine text files representing three regional languages (Javanese, Minangkabau, and Sundanese) and three stemming approaches (ECS, Porter, and Porter Hybrid-ECS). Each file contains pairs of source sentences and outputs produced through the NMT → Stemming → NMT pipeline: regional-language text is translated into Indonesian, stemmed, and then projected back into the original regional language. The parallel corpora were aligned at the lexical level and curated to balance token counts, structural variation, and morphological diversity.
Experimental results reveal several prominent findings. ECS excels for languages with complex affixation because it captures local morphological patterns; Porter is computationally lighter but tends to under-stem prefixes/infices typical of regional languages; and Porter Hybrid (ECS) consistently performs best across all three languages, yielding an average accuracy gain of approximately 4–7% over single models. Evaluation uses precision–recall (assessing correctness and coverage of recovered lemmas), cosine similarity (semantic proximity to reference forms), and BLEU score (n-gram similarity to reference).
Interpretively, the data indicate that a hybrid approach blending linguistic insight (ECS) with a general algorithm (Porter) within an NMT-based pipeline produces a more universal and adaptive stemming system for Indonesian regional languages. These findings are relevant for developing cross-regional NLP applications (machine translation, sentiment analysis, lexical normalization), training models for low-resource languages, and improving text preprocessing in translation systems to achieve more accurate morphology-level handling.
DOI: 10.17632/957xy4kmyt
title: life satisfaction, friendship support and self-esteem

description: A total of 882 participants fully participated in all survey sessions of this study. Among the 882 surveyed students, there were 462 male students, accounting for 52.4%, and 420 female students, accounting for 47.6%. In this study, the average age of participants at the time of the first test was (12.20 ± 0.49) years old. After one year of follow-up survey, the sample attrition rate ranged from 6% to 17%, with the main reasons for attrition including students transferring schools, taking leave for various reasons, and refusing to continue participating in the questionnaire survey.
DOI: 10.15468/dl.nuxs5q
title: Occurrence Download

description: A dataset containing 3507553 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Philippines",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 3507553 records from 1801 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002184-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15242279
title: Moglia et al., 2025_Long-Term Monitoring of Hydrozoan Fauna of the Portofino Promontory

description: Hydroid_dataset.xlsx contains raw data of hydrozoan species collected in 1980, 2004, and 2024 along a bathymetric transect at the Portofino Promontory (Ligurian Sea). Figures/graphs generated are also included.- Hydroid_metadata.txt: description of data in each Excel sheet.
DOI: 10.5281/zenodo.15242280
title: Moglia et al., 2025_Long-Term Monitoring of Hydrozoan Fauna of the Portofino Promontory

description: Hydroid_dataset.xlsx contains raw data of hydrozoan species collected in 1980, 2004, and 2024 along a bathymetric transect at the Portofino Promontory (Ligurian Sea). Figures/graphs generated are also included.- Hydroid_metadata.txt: description of data in each Excel sheet.