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503 results for “Indonesian”
Figs. 13, 14 in Notes On Southeast Asian Ranatra Heteroptera Nepidae , With Description Of A New Species From Singapore And Neighbouring Indonesian Islands
Figs. 13, 14. Ranatra rafflesi, left foreleg, inner lateral view. 12: Male (holotype, TAD0830). 13: Female, specimen from Singapore, Upper Seletar Reservoir (TAD0827).
Figs. 9, 10 in Notes On Southeast Asian Ranatra Heteroptera Nepidae , With Description Of A New Species From Singapore And Neighbouring Indonesian Islands
Figs. 9, 10. Ranatra species, male phallotheca, left lateral view. 9: R. longipes longipes, specimen from Singapore, Nee Soon Swamp Forest (Y843). 10: R. rafflesi, specimen from Singapore, Upper Seletar Reservoir (TAD0830).
Figs. 11, 12 in Notes On Southeast Asian Ranatra Heteroptera Nepidae , With Description Of A New Species From Singapore And Neighbouring Indonesian Islands
Figs. 11, 12. Ranatra species, left foreleg, outer lateral view. 11: R. longipes longipes, male specimen from Singapore, Nee Soon Swamp Forest (Y843). 12: R. rafflesi, specimen from Singapore, Upper Seletar Reservoir (TAD0830).
Figs. 1–8 in Notes On Southeast Asian Ranatra Heteroptera Nepidae , With Description Of A New Species From Singapore And Neighbouring Indonesian Islands
Figs. 1–8. Ranatra species, male left parameres with details of paramere apex. 1, 2: R. longipes longipes, specimen from Indonesia, Borneo, East Kalimantan (CL 2092). 3, 4: R. longipes longipes, specimen from Singapore, Nee Soon Swamp Forest (Y843). 5, 6: R. thai, specimen from Thailand, Chiang Mai Prov (CL 2201). 7, 8: R. rafflesi, specimen from Singapore, Seletar Reservoir (TAD0830).
IndQNER: Indonesian Benchmark Dataset from the Indonesian Translation of the Quran
<h2>IndQNER</h2> <p>IndQNER is a Named Entity Recognition (NER) benchmark dataset that was created by manually annotating 8 chapters in the Indonesian translation of the Quran. The annotation was performed using a web-based text annotation tool, <a href="https://www.tagtog.com/" target="_blank" rel="noopener">Tagtog</a>, and the BIO (Beginning-Inside-Outside) tagging format. The dataset contains:</p> <ul> <li>3117 sentences</li> <li>62027 tokens</li> <li>2475 named entities</li> <li>18 named entity categories</li> </ul> <h2>Named Entity Classes</h2> <p>The named entity classes were initially defined by analyzing the existing Quran concepts ontology. The initial classes were updated based on the information acquired during the annotation process. Finally, there are 20 classes, as follows:</p> <ol> <li>Allah</li> <li>Allah's Throne</li> <li>Artifact</li> <li>Astronomical body</li> <li>Event</li> <li>False deity</li> <li>Holy book</li> <li>Language</li> <li>Angel</li> <li>Person</li> <li>Messenger</li> <li>Prophet</li> <li>Sentient</li> <li>Afterlife location</li> <li>Geographical location</li> <li>Color</li> <li>Religion</li> <li>Food</li> <li>Fruit</li> <li>The book of Allah</li> </ol> <h2>Annotation Stage</h2> <p>There were eight annotators who contributed to the annotation process. They were informatics engineering students at the State Islamic University Syarif Hidayatullah Jakarta.</p> <ol> <li>Anggita Maharani Gumay Putri</li> <li>Muhammad Destamal Junas</li> <li>Naufaldi Hafidhigbal</li> <li>Nur Kholis Azzam Ubaidillah</li> <li>Puspitasari</li> <li>Septiany Nur Anggita</li> <li>Wilda Nurjannah</li> <li>William Santoso</li> </ol> <h2>Verification Stage</h2> <p>We found many named entity and class candidates during the annotation stage. To verify the candidates, we consulted Quran and Tafseer (content) experts who are lecturers at Quran and Tafseer Department at the State Islamic University Syarif Hidayatullah Jakarta.</p> <ol> <li>Dr. Eva Nugraha, M.Ag.</li> <li>Dr. Jauhar Azizy, MA</li> <li>Dr. Lilik Ummi Kultsum, MA</li> </ol> <h2>Evaluation</h2> <p>We evaluated the annotation quality of IndQNER by performing experiments in two settings: supervised learning (BiLSTM+CRF) and transfer learning (<a href="https://huggingface.co/indobenchmark/indobert-base-p1" target="_blank" rel="noopener">IndoBERT</a> fine-tuning).</p> <h3>Supervised Learning Setting</h3> <p>The implementation of BiLSTM and CRF utilized <a href="https://huggingface.co/indobenchmark/indobert-base-p1" target="_blank" rel="noopener">IndoBERT</a> to provide word embeddings. All experiments used a batch size of 16. These are the results:</p> <table> <tbody> <tr> <td>Maximum sequence length</td> <td>Number of e-poch</td> <td>Precision</td> <td>Recall</td> <td>F1 score</td> </tr> <tr> <td>256</td> <td>10</td> <td>0.94</td> <td>0.92</td> <td>0.93</td> </tr> <tr> <td>256</td> <td>20</td> <td> 0.99</td> <td>0.97</td> <td>0.98</td> </tr> <tr> <td>256</td> <td>40</td> <td>0.96</td> <td>0.96</td> <td>0.96</td> </tr> <tr> <td>256</td> <td>100</td> <td>0.97</td> <td>0.96</td> <td>0.96</td> </tr> <tr> <td>512</td> <td>10</td> <td>0.92</td> <td>0.92</td> <td>0.92</td> </tr> <tr> <td>512</td> <td>20</td> <td>0.96</td> <td>0.95</td> <td>0.96</td> </tr> <tr> <td>512</td> <td>40</td> <td>0.97</td> <td>0.95</td> <td>0.96</td> </tr> <tr> <td>512</td> <td>100</td> <td>0.97</td> <td>0.95</td> <td>0.96</td> </tr> </tbody> </table> <h3>Transfer Learning Setting</h3> <p>We performed several experiments with different parameters in IndoBERT fine-tuning. All experiments used a learning rate of 2e-5 and a batch size of 16. These are the results:</p> <table> <tbody> <tr> <td>Maximum sequence length</td> <td>Number of e-poch</td> <td>Precision</td> <td>Recall</td> <td>F1 score</td> </tr> <tr> <td>256</td> <td>10</td> <td>0.67</td> <td>0.65</td> <td>0.65</td> </tr> <tr> <td>256</td> <td>20</td> <td> 0.60</td> <td>0.59</td> <td>0.59</td> </tr> <tr> <td>256</td> <td>40</td> <td>0.75</td> <td>0.72</td> <td>0.71</td> </tr> <tr> <td>256</td> <td>100</td> <td>0.73</td> <td>0.68</td> <td>0.68</td> </tr> <tr> <td>512</td> <td>10</td> <td>0.72</td> <td>0.62</td> <td>0.64</td> </tr> <tr> <td>512</td> <td>20</td> <td>0.62</td> <td>0.57</td> <td>0.58</td> </tr> <tr> <td>512</td> <td>40</td> <td>0.72</td> <td>0.66</td> <td>0.67</td> </tr> <tr> <td>512</td> <td>100</td> <td>0.68</td> <td>0.68</td> <td>0.67</td> </tr> </tbody> </table> <p> </p> <p>This dataset is also part of the <a href="https://github.com/IndoNLP/nusa-crowd" target="_blank" rel="noopener">NusaCrowd project</a> which aims to collect Natural Language Processing (NLP) datasets for Indonesian and its local languages.</p> <h2>How to Cite</h2> <p>@InProceedings{10.1007/978-3-031-35320-8_12,<br>author="Gusmita, Ria Hari<br>and Firmansyah, Asep Fajar<br>and Moussallem, Diego<br>and Ngonga Ngomo, Axel-Cyrille",<br>editor="M{\'e}tais, Elisabeth<br>and Meziane, Farid<br>and Sugumaran, Vijayan<br>and Manning, Warren<br>and Reiff-Marganiec, Stephan",<br>title="IndQNER: Named Entity Recognition Benchmark Dataset from the Indonesian Translation of the Quran",<br>booktitle="Natural Language Processing and Information Systems",<br>year="2023",<br>publisher="Springer Nature Switzerland",<br>address="Cham",<br>pages="170--185",<br>abstract="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.",<br>isbn="978-3-031-35320-8"<br>}</p> <h2>Contact</h2> <p>If you have any questions or feedback, feel free to contact us at ria.hari.gusmita@uni-paderborn.de or ria.gusmita@uinjkt.ac.id</p>
Fig. 2 in Why many Indonesian marine species remain undescribed: a case study using polychaete species discovery
Fig. 2. The composition of active local taxonomists. Blue, black, white, and grey bars indicate marine, faunal, floral, and microbial taxa, respectively (the last three taxa are non-marine). A list of the taxonomists is provided in Appendix 1.
Fig. 3 in Why many Indonesian marine species remain undescribed: a case study using polychaete species discovery
Fig. 3. Distributional map of Indonesian marine species exclusively described by local taxonomists. Grey and white land areas indicate Indonesia and the neighbouring countries, respectively.
Fig. 1 in Why many Indonesian marine species remain undescribed: a case study using polychaete species discovery
Fig. 1. The cumulative number of Indonesian polychaete species described (the black solid line), projected to the end of the 21st century with a 95% probability. The upper and lower red dashed lines, as well as the red solid line, are the projected species discovery trends assuming great, little, and medium taxonomic efforts, respectively, being made to shift the pre-existing trend.
Figure 3. A third C in First photographic inland record of blacktip reef sharks Carcharhinus melanopterus (Carcharhiniformes: Carcharhinidae) in Indonesian waters
Figure 3. A third C. melanopterus caught by local people on 6 March 2019 in Sentani, Papua province, Indonesia (Photo: Jay Fajar).
Figure 1 in First photographic inland record of blacktip reef sharks Carcharhinus melanopterus (Carcharhiniformes: Carcharhinidae) in Indonesian waters
Figure 1. Location of C. melanopterus (yellow triangle) found in freshwater habitat in Sentani, Papua province, Indonesia.
Elementary School Indonesian Exam Questions with Bloom's Taxonomy Dataset
<p>This is the dataset used in "Indonesian Exam Question Classification Based on Bloom’s Taxonomy using Fine-Tuned IndoBERT" research</p>
Preprocessed Indonesian Twitter Dataset on UU Perlindungan Data Pribadi for Sentiment Analysis Research
<p>This dataset, titled 'Preprocessed Indonesian Twitter Dataset on UU Perlindungan Data Pribadi for Sentiment Analysis Research,' is curated and prepared for the purpose of conducting sentiment analysis research as outlined in the project 'ANALISIS SENTIMEN MASYARAKAT TERHADAP UU PERLINDUNGAN DATA PRIBADI PADA APLIKASI X DENGAN METODE SUPPORT VECTOR MACHINE' (Sentiment Analysis of the Community Towards the Personal Data Protection Law on Application X Using Support Vector Machine Method).</p>
Traits data of naturalized and non-naturalized alien species of four Indonesian Botanic Gardens
Open the record for dataset details and reuse information.
Phylogenomics of white-eyes, a 'great speciator,' reveals Indonesian archipelago as the center of lineage diversity
<p><span><span><span><span><span><span><span><span><span><span><span>Archipelagoes serve as important 'natural laboratories' which facilitate the study of island radiations and contribute to the understanding of evolutionary processes. The white-eye genus <i>Zosterops </i>is a classical example of a 'great speciator', comprising c. 100 species from across the Old World, most of them insular. We achieved an extensive geographic DNA sampling of <i>Zosterops </i>by using historical specimens and recently collected samples. Using over 700 genome-wide loci in conjunction with coalescent species tree methods and gene flow detection approaches, we untangled the reticulated evolutionary history of <i>Zosterops</i>, which comprises three main clades centered in Indo-Africa, Asia, and Australasia, respectively. Genetic introgression between species permeates the <i>Zosterops</i>phylogeny, regardless of how distantly related species are. Crucially, we identified the Indonesian archipelago, and specifically Borneo, as the major centre of diversity and the only area where all three main clades overlap, attesting to the evolutionary importance of this region. </span></span></span></span></span></span></span></span></span></span></span></p>
Risk factors of typhoid infection in the Indonesian archipelago
<p>Datasets and accompanying questionnaires for the epidemiological study on risk factors for typhoid infection conducted between June 2010 and June 2011 from 14 selected hospitals and health centres in three Indonesian islands: in and around Makassar in South Sulawesi (Sulawesi); Jayapura (Papua); Samarinda in East-Kalimantan (Kalimantan).</p> <p>The mansucript describing methods and results has been accepted for publication in PLOS ONE "Risk factors of typhoid infection in the Indonesian archipelago" by Sandra Alba, Mirjam I Bakker, Mochammad Hatta, Pauline FD Scheelbeek, Ressy Dwiyanti<sup> </sup>, Romi Usman, Andi R Sultan, Muhammad Sabir, Nataniel Tandirogan, Masyhudi Amir, Yadi Yasir, Rob Pastoor, Stella van Beers,Henk L Smits</p>
Data on digitalization in Indonesian provinces
<p>Data on digitalization in Indonesian provinces is the collection of the data for variables affecting the level of digitalization in Indonesian provinces measured by the use of Indonesian Digital Society Index. </p>
Dataset Indonesian News
<p>Dataset for paper "Identifikasi Topik Hangat Di Media Berita Menggunakan Pendekatan Natural Language Processing". This dataset include news data from Antara, CNBC, CNN Indonesia JPNN, Kumparan, Merdeka, Okezone, Republika, Sindonews, Suara, Tempo, and Tribun. This dataset retrived at 16 september 2023</p>
Developing National Identity Scale: As Indonesian Case
<p><strong><em>Background: </em></strong></p> <p><em>National identity is an important concept to be developed, so it is necessary to have a measurement instrument with evidence of validity and reliability. Thus, the accuracy of the information obtained through the measurement of this instrument will be guaranteed.</em></p> <p> </p> <p><strong><em>Objective:</em></strong></p> <p><em>This article aims to test the psychometric properties of the national identity scale to fulfil the standardization of good measurement.</em></p> <p> </p> <p><strong><em>Method:</em></strong></p> <p><em>In this study, three analytical approaches were used: content validity involving eight subject matter experts (SME), confirmatory factor analysis (CFA), and reliability analysis. For the reliability and CFA approach, 300 students were used as samples taken through the convenience sampling technique.</em></p> <p> </p> <p><strong><em>Result:</em></strong></p> <p><em>The analysis results show that out of the 20 items compiled in the content validity analysis, one item is inadequate because it has a below standard score on Aiken's coefficient of validity. This one item was revised again to be included in the tryout and data collection for the following analysis stage. The results of the CFA analysis show that the model and data are fit, and there are two inadequate items. At the same time, the contribution of the three factors on the national identity scale is significant. In the third analysis, it was found that adequate coefficient reliability affected the low standard error measurement (SEM).</em></p> <p> </p> <p><strong><em>Conclusion:</em></strong></p> <p><em>Based on the analysis results, it can be concluded that the Indonesian national identity scale has fulfilled good psychometric properties. It can be proven from evidence validity based on content, confirmatory factor analysis (CFA), and reliability analysis.</em></p>
GRETIL - Göttingen Register of Electronic Texts in Indian Languages. INDONESIAN TEXTS
<p>GRETIL - Göttingen Register of Electronic Texts in Indian Languages. INDONESIAN TEXTS</p>
Species matrix of Indonesian litter and soil Collembola with environmental factors
<p>Collembola are among the most abundant and diverse microarthropods that affect litter decomposition, control microbial functioning and support invertebrate predators. Rainforest conversion and expansion of plantations in tropical regions are associated with changes in microclimate and biodiversity decline, but information on the impact of tropical land use on Collembola communities and their seasonal fluctuations is very limited. Here, we investigated seasonal fluctuations in density and community composition of Collembola in rainforest, and in rubber and oil palm plantations in Jambi province (Sumatra, Indonesia), a region with moderate seasonality (wet and dry seasons) that experienced one of the strongest deforestations globally during the last decades. Samples were taken at the end of the wet season (March), the beginning of the dry season (June), the end of the dry season (August) and the beginning of the wet season (November). Collembola density in the litter was at a maximum at the beginning of the wet season, whereas in soil it generally varied little. Densities of euedaphic (soil-adapted) and epedaphic species (upper litter-adapted) fluctuated less with season than hemiedaphic (lower litter-adapted) and atmobiotic species (aboveground-adapted). Collembola community composition changed with season in each of the land-use systems, with the differences being more pronounced in litter than in soil. Differences in community composition among all three land-use systems were most pronounced at the beginning of the dry season. Water content, pH, fungal and bacterial biomarkers, C/N ratio and root biomass were identified as factors related to seasonal variations in species composition of Collembola in different systems. Our results show that Collembola density and community composition often fluctuate in a similar way across land-use systems, indicating that they are driven by common environmental factors, with water content, pH and food availability being of prominent importance. Notably, differences in Collembola community composition between rainforest and plantation systems were most pronounced in the dry season (June, August), indicating that the conversion of rainforest into plantation systems aggravates detrimental effects of low moisture on soil animal communities. Fluctuations in Collembola communities were generally stronger in litter than in soil, reflecting that Collembola in litter are less buffered against climatic variations than those in soil. Overall, the results document the sensitivity of tropical soil animal communities to seasonal climatic variations, which <span>intensifies</span> the effects of the conversion of rainforest into plantation systems on soil biodiversity.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.