Find research datasets worth reusing

Figure 3 in A revised genus-level classification for the Neotropical groups of the cleptoparasitic bee tribe Sphecodini Schenck (Hymenoptera, Apidae, Halictinae)

Figure 3 Nesosphecodes depressus sp. nov. Female Holotype. A) habitus, B) head in frontal view, C) mesosoma in dorsal view, D) metasoma in dorsal view. B and C under the same scale.

Figure 5 in A revised genus-level classification for the Neotropical groups of the cleptoparasitic bee tribe Sphecodini Schenck (Hymenoptera, Apidae, Halictinae)

Figure 5 Nesosphecodes depressus sp. nov. Male Paratype. A) T6, B) S7 and S8, C) genital capsule, ventral view, D) genital capsule, dorsal view. All images under the same scale.

Figure 4 in A revised genus-level classification for the Neotropical groups of the cleptoparasitic bee tribe Sphecodini Schenck (Hymenoptera, Apidae, Halictinae)

Figure 4 Nesosphecodes depressus sp. nov.Male Paratype. A) habitus, B) head in frontal view, C) mesosoma in dorsal view, D) metasoma in dorsal view.B and C under the same scale.

Figure 1 Melissocleptis gen. nov. and Austrosphecodes. A in A revised genus-level classification for the Neotropical groups of the cleptoparasitic bee tribe Sphecodini Schenck (Hymenoptera, Apidae, Halictinae)

Figure 1 Melissocleptis gen. nov. and Austrosphecodes. A) Melissocleptis capriciosus, female head, colored bars indicating the scape and frons length, B) M. capriciosus male head, F1–3 colored; C) M. capriciosa male metasoma, pygidial plate indicated in blue; D) Austrosphecodes brasiliensis, female head, colored bars indicating the scape and frons length, E) A. brasiliensis male head, F1–3 colored; F) A. brasiliensis male metasoma, pygidial plate indicated in blue. All images under the same scale.

Dataset used in the publication "Using of Transformers Models for Text Classification to Mobile Educational Applications"

Dataset used in the publication "Using of Transformers Models for Text Classification to Mobile Educational Applications". More info about the dataset can be found in the published article.

IEEE ICME 2024 Grand Challenge: Semi-supervised Acoustic Scene Classification under Domain Shift Evaluation Dataset

The Chinese Acoustic Scene (CAS) 2023 dataset is a large-scale dataset that serves as a foundation for research related to environmental acoustic scenes. The dataset includes 10 common acoustic scenes, with a total duration of over 130 hours. Each audio clip is 10 seconds long with metadata about the recording location and timestamp. The dataset was collected by members of the Joint Laboratory of Environmental Sound Sensing at the School of Marine Science and Technology, Northwestern Polytechnical University. The data collection period spanned from April 2023 to September 2023, covering 22 different cities across China. The CAS 2023 dataset was collected using the XS-SN-2BE1 manufactured by Xi'an Lianfeng Acoustic Technologies Co., Ltd (https://www.lfxstek.com/).   The ICME 2024 Semi-supervised Acoustic Scene Classification under Domain Shift challenge (https://2024.ieeeicme.org/grand-challenge-proposals/, https://ascchallenge.xshengyun.com/) dataset consists of development (https://zenodo.org/records/10616533) and evaluation datasets, all derived from the CAS 2023 dataset. The evaluation dataset includes 1,100 recordings, where data are selected from 12 cities, with 5 unseen cities specifically chosen to provide a more comprehensive evaluation of submissions under domain shift. Baseline: https://github.com/JishengBai/ICME2024ASC Acoustic scenes (10): Bus, Airport, Metro, Restaurant, Shopping mall, Public square, Urban park, Traffic street, Construction site, Bar

Irish Rumour Stance Classification Dataset

We create a new public test set for rumour stance classification with substantial differences from the <a href="https://aclanthology.org/S19-2147/">RumourEval dataset</a> over both vocabulary and stance distribution, making it well-suited for studies on evaluation or adaptation under domain shift.

Data augmentation for Multi-Classification of Non-Functional Requirements - Dataset

There are four datasets: 1.Dataset_structure indicates the structure of the datasets, such as column name, type, and value. 2. Spanish_promise_exp_nfr_train and Spanish_promise_exp_nfr_test are the non-functional requirements of the Promise_exp[1] dataset translated into the Spanish language. 3. Balanced_promise_exp_nfr_train is the new balanced dataset of Spanish_promise_exp_nfr_train, in which the Data Augmentation technique with chatGPT was applied to increase the requirements with little data and random undersampling was used to eliminate requirements. The labeling schema, similar to PROMISE NFR, includes the following categories: A: Availability, PO: Portability, L: Legal, FT: Fault tolerance, SC: Scalability, MN: Maintainability, LF: Look and feel, PE: Performance, O: Operational. US: Usability, and SE: Security.

Train and Evaluation Code, Road Classification Models and Test set of the paper "Impact of Image Resolution and Image Overlap on the Prediction Performance of Convolutional Neural Networks Trained for Road Classification"

This repository contains the Python scripts built for training and evaluation of the implementation, together with the test data and the resulting road classification models corresponding to the paper "Impact of Image Resolution and Image Overlap on the Prediction Performance of Convolutional Neural Networks Trained for Road Classification". The scripts make use of the Tensorflow with Keras framework and the additional required dependencies. The training and validation set is based on the binary SROADEX dataset (https://zenodo.org/records/6482346) that was re-split into tiles that feature the image resolutions (256 x 256, 512 x 512, and 1024 x 1024 pixels) and image overlaps (0% and 12.5%) considered in this study. The data have been generated using scripts developed in Python using Open Source libraries (GDAL/OGR and MapScript) for rasterization of vector cartography that represents the axes of the different types of roads (urban, interurban and rural). This binary road data contains information from 16 full orthoimages (28.5 km * 18.5 km) with spatial resolution of 0.5 m/pixel from the insular and peninsular Spanish territory. Due to the size on disk of approximately 546 gigabytes, this training and validation data is only available upon request from the corresponding author. The test set has been generated from a novel area of 28.5 km * 18.5 km and features binary road labels. The test sets are provided in the repository for each resolution (with no overlap), so that additional DL models can be evaluated on the same data and compared with the results achieved in this study. The structure of the information shared in this repository is as follows: The scripts have been grouped by tile resolution (256, 512 and 1024). First, the test set and the evaluation script can be found. For each tile resolution, there are two subfolders (corresponding to the "no overlap" and "12.5% overlap"). In each case, the Python scripts for training the models in the three repetitions are shared, and the trained models (H5 format) are shared in compressed form. Finally, for each resolution we also share the testing dataset which consists of two folders. The material is distributed under a CC-BY 4.0 license.

opencc-by-4.0Apr 2023View details →

Source classification for OM SUSS 5.0

Since the launch of the XMM in 1999 OM images in the six filters have been obtained which are used to construct the SUSS catalogue and the Epoch of observation in the catalogue spans therefore close to 24 years by now. With a positional accuracy of 0.5" some nearby stars show proper motion and it is useful to compute the Epoch 2000 positions for those. The positions are otherwise reported on the (J2000) ICRS system. The SUSS catalogue has two parts. The first table, called SUMMARY contains the information for each observation. For each observation this table contains the start and end date of the observation, the pointing direction, the exposure time for each of the OM filters and some other parameters. The second table lists all the SOURCES found in all of the images, the magnitudes in different filters, magnitude errors, significance, source extent, quality flags, and a link to the information in the SUMMARY. The epoch of observation can be derived from the start and end time of the observation in the SUMMARY table. Each row in the SOURCES table can be matched to the observation in the SUMMARY and thus be given the epoch of observation. Once the epoch for an observation is known, the observation can be matched to the Gaia DR3 list of high proper motion objects transformed to the time of the SUSS observation. In order to do this, the following procedure was used: (1) the sources with proper motions larger than 25 mas/yr and absolute PM/error larger than 10 were extracted from the Gaia DR3 catalogue. (2) for each SUSS observation the Gaia DR3 sources within 15 arcminutes of the center of that field were selected. (3) the selected Gaia DR3 were transformed to the epoch of the SUSS observation. (4) the SUSS sources were then matched using XArches[2] to those equal-epoch sources from Gaia-DR3. XArches also provides a probability that of a good match between a SUSS and Gaia DR3 source. Only sources with a probability of good match greater than 0.5 were retained. (5) the Epoch 2000 positions were computed for the matches. (6) after all SUSS observations were processed, the positions of sources which had not been identified as having a high PM were added to the Epoch 2000 positions, so that for all SUSS sources Epoch 2000 position were defined. For clarification, all SUSS positions were J2000, even if at the observed Epoch.

opencc-by-4.0Sep 2022View details →

Automatic Classification of Final Assignments at the Nuclear Polytechnic Library

This study aimed to look for a method to automatically classify the final projects of Indonesian Nuclear Technology Polytechnic students.

Fig. 4 in New species of mirid insects and their importance for the higher classification of plant bugs

Fig. 4. Photographs (A1, A3) and drawings (A2, A4) of femoral trichobothria in mirid insect Metoisops akingbohungbei Herczek and Popov, 2014, holotype male, CEHI BB M HE 4, from the Baltic Amber (unknown locality on Baltic Sea Coast), mid-Eocene. Five mesofemoral trichobothria (A1, A2); six metafemoral trichobothria (A3, A4). Scale bars 0.1 mm.

Fig. 3 in New species of mirid insects and their importance for the higher classification of plant bugs

Fig. 3. Mirid insect Metoisops popovi Kim, Taszakowski, and Jung, sp. nov., holotype female, CNU CNUHHMF005, from the Baltic Amber (unknown locality on Baltic Sea Coast), mid-Eocene. Dorsal habitus. Arrow points to deep incision between calli (A1), lateral habitus (A2), head in dorsal view (A3), head in lateral view (A4), scutellum (A5), hindfemur with trichobothria (A6).

Fig. 2 in New species of mirid insects and their importance for the higher classification of plant bugs

Fig. 2. Mirid insect Metoisops michalskii Kim, Taszakowski, and Herczek sp. nov., holotype male, DZUS HE44-451-1-001, from the Baltic Amber Gdańsk Bay, Poland), mid-Eocene. Dorsal habitus (A1), lateral habitus (A2), head and thorax in lateral view and antennal structure (A3), abdomen and legs in lateral view (A4), hindtarsus (A5), genital segment with parameres (A6). Abbreviations: i, first antennal segment; ii, second antennal segment; iii, third antennal segment; iv, fourth antennal segment; iv-1, first subsegment of fourth antennal segment; iv-2, second subsegment of fourth antennal segment.

Fig. 1 in New species of mirid insects and their importance for the higher classification of plant bugs

Fig. 1. Baltic ambers with specimens of mirid insects. A. Metoisops michalskii Kim, Taszakowski, and Herczek sp. nov., holotype male, DZUS HE44- 451-1-001, from the Baltic Amber (Vistula Spit, Gdańsk Bay, Poland), mid-Eocene. B. Metoisops popovi Kim, Taszakowski, and Jung sp. nov., holotype female, CNU CNUHHMF005, from the Baltic Amber (unknown locality on Baltic Sea Coast), mid-Eocene.

opencc-by-4.0Mar 2016View details →

Fig. 2 in A closer look at the main actors of Neotropical floodplain food webs: functional classification and niche overlap of dominant benthic invertebrates in a floodplain lake of Paraná River

Fig. 2. Cluster plot depicting trophic similarity (Morisita index) among species of dominant benthic invertebrates in a floodplain lake of ParanÁ River, Argentina. Dotted line depicts the threshold similarity of 0.6.

opencc-by-4.0Mar 2016View details →

Fig. 3 in A closer look at the main actors of Neotropical floodplain food webs: functional classification and niche overlap of dominant benthic invertebrates in a floodplain lake of Paraná River

Fig. 3. Non Metric Multidimensional scaling plot. Circles depicts taxa classified as gatherer collectors (Aulodrilus pigueti, Pristina leidyi, Dero vagus, Nais communis, Pelomus sp., Cladopelma sp., Endotribelos sp., Polypedilum sp., Chironomus sp., Parachironomus sp., Phaenopsectra sp., Americabaetis sp., Baetis sp., Campsurus violaceus, Hyalella curvispina, Crynellus sp.) [Triangles: Tanypodinae (Coelotanypus sp., Procladius sp. and Ablabesmyia (Karelia); inverted triangle: Sympetrum sp.; square: Monopelopia sp.; cross: Pomacea canaliculata].

opencc-by-4.0Mar 2016View details →

Fig. 1 in A closer look at the main actors of Neotropical floodplain food webs: functional classification and niche overlap of dominant benthic invertebrates in a floodplain lake of Paraná River

Fig. 1. Relative importance (IRI) of food items for analyzed taxa of dominant benthic invertebrates in a floodplain lake of ParanÁ River, Argentina (parenthesis indicate sample size).

opencc-by-4.0Jan 2019View details →

→ Fig. 10. FESEM images of the test structure in lagenid foraminifers from Recent, Admiralty Bay, King George Island, West Antarctica (A) and from the Jurassic of Gnaszyn, Poland (B, C). A. Unilocular Procerolagena gracilis Williamson, 1848, MWGUW ZI/67/44/02. B. Unilocular Lagena globosa Montagu, 1803, MWGUW ZI/67/61/09. C. Uniserial Nodosaria pulchra Franke, 1936, MWGUW ZI/67/61/26. Oblique cross-sectional views (A1, A2, A4, B1, B2, C); transverse cross-sectional views, showing single-crystal interlocked bundle structures, inner pores which extend along the entire length of the bundles as well as prominent calcite cleavage (A3, B3). Abbreviations: c, prominent calcite cleavage; ip, inner pore. in Chamber arrangement versus wall structure in the high-rank phylogenetic classification of Foraminifera

→ Fig. 10. FESEM images of the test structure in lagenid foraminifers from Recent, Admiralty Bay, King George Island, West Antarctica (A) and from the Jurassic of Gnaszyn, Poland (B, C). A. Unilocular Procerolagena gracilis Williamson, 1848, MWGUW ZI/67/44/02. B. Unilocular Lagena globosa Montagu, 1803, MWGUW ZI/67/61/09. C. Uniserial Nodosaria pulchra Franke, 1936, MWGUW ZI/67/61/26. Oblique cross-sectional views (A1, A2, A4, B1, B2, C); transverse cross-sectional views, showing single-crystal interlocked bundle structures, inner pores which extend along the entire length of the bundles as well as prominent calcite cleavage (A3, B3). Abbreviations: c, prominent calcite cleavage; ip, inner pore.