cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2h5l5g
title: CCDC 2299275: Experimental Crystal Structure Determination

description: Related Article: Fateme Molajafari, Tianrui Li, Mehrnaz Abbasichaleshtori, Moein Hajian Z. D, Anthony F. Cozzolino, Daniel R. Fandrick, Joshua D. Howe |2024|CrystEngComm|26|1620|doi:10.1039/D3CE01252B
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.15468/dl.jy8vps
title: Occurrence Download

description: A dataset containing 103 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Galianthe spicata (Miq.) Cabaña Fader & Dessein"
 ]
}

 The dataset includes 103 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0138955-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vnzjht
title: Occurrence Download

description: A dataset containing 10 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Italy",
 "TaxonKey is Anemone palmata L.",
 "Year 1969-2020"
 ]
}

 The dataset includes 10 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002681-240130105604617/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9jbgp7
title: Occurrence Download

description: A dataset containing 11438 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Dicranella rufescens (Dicks.) Schimp., Dicranella staphylina H.Whitehouse)",
 "Geometry POLYGON((-9.36035 44.71551,-10.59082 42.81152,-11.82129 36.80928,-7.69043 36.17336,-3.38379 35.88905,3.12012 38.13456,9.18457 38.20366,11.7334 37.16032,12.78809 34.74161,17.09473 33.79741, 23.15918 34.23451,30.10254 32.62087,33.2666 32.99024,35.72754 35.03,33.00293 35.53223,28.69629 35.17381,28.34473 36.31513,25.7959 37.50973,24.91699 39.90974,28.78418 40.6473,30.71777 41.17865,36.87012 45.213,38.18848 46.98025,38.71582 47.7541,39.94629 47.93107,40.20996 49.61071,37.0459 50.34546,35.72754 50.40152,33.79395 52.26816,31.68457 51.94426,31.4209 53.27835,32.56348 53.43572,30.98145 54.77535,30.89355 55.52863,28.43262 56.02295,27.20215 57.65716,27.37793 58.30949,27.90527 59.17593,29.66309 59.88894,27.6416 60.58697,31.50879 62.75473,28.78418 69.83962,30.89355 70.28912,29.22363 71.55274,21.75293 71.30079,14.28223 70.14036,11.38184 67.47492,4.87793 63.66576,-5.53711 71.52491,-10.63477 71.69129,-21.5332 67.74276,-28.56445 65.36684,-24.3457 61.35461,-9.36035 44.71551))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2021"
 ]
}

 The dataset includes 11438 records from 99 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0239231-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yut9u9
title: Occurrence Download

description: A dataset containing 91 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Galactia filiformis (Jacq.) Benth."
 ]
}

 The dataset includes 91 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010810-230810091245214/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.x4s2yg
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Taeniophyllum brachypus Schltr."
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004115-230810091245214/datasets/export for details.
DOI: 10.15468/dl.e9bk4c
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "BasisOfRecord is Observation",
 "BasisOfRecord is Machine Observation",
 "BasisOfRecord is Human Observation",
 "BasisOfRecord is Occurrence evidence"
 ]
 },
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Trogopterus xanthipes (Milne-Edwards, 1867)",
 "Year 1972-2022"
 ]
}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0313208-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/prrq53
title: Plan régional d'actions en faveur des odonates (PRAO) : 3ème phase (2017 - 2018) menée par le CEN Aquitaine-Observations d'odonates réalisées lors de journées de prospections animées par la LPO Aquitaine

description: Quatre camps et deux journées de prospection ont été organisés en 2017 et 2018 dans le cadre de l’animation de l’atlas des odonates d’Aquitaine.
Les secteurs sous-prospectés ont été ciblés en priorité, à savoir : l’ouest et le sud-est de la Dordogne, l’est des Landes, le Lot-et-Garonne et certains secteurs des Pyrénées béarnaises
DOI: 10.15468/dl.2ehydb
title: Occurrence Download

description: A dataset containing 334076 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation, Material sample, Material citation, Specimen)",
 "Continent is South America",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Squamata",
 "Year 1900-2023"
 ]
}

 The dataset includes 334076 records from 580 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012448-230810091245214/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13130/rd_unimi/ui9ecn/hu7hgh
title: Giugno_1880_Pagina_041.jpg

description: Bulletin of 1880/06/11
DOI: 10.13130/rd_unimi/ui9ecn/zqjafz
title: Giugno_1880_Pagina_042.jpg

description: Bulletin of 1880/06/11
DOI: 10.13130/rd_unimi/ui9ecn/pipvem
title: Giugno_1880_Pagina_043.jpg

description: Bulletin of 1880/06/11
DOI: 10.13130/rd_unimi/ui9ecn/65jk4u
title: Giugno_1880_Pagina_044.jpg

description: Bulletin of 1880/06/11
DOI: 10.13130/rd_unimi/ui9ecn/elzgy0
title: Giugno_1880_Pagina_045.jpg

description: Bulletin of 1880/06/12
DOI: 10.13130/rd_unimi/ui9ecn/wqft0b
title: Giugno_1880_Pagina_046.jpg

description: Bulletin of 1880/06/12
DOI: 10.13130/rd_unimi/ui9ecn/3vplbj
title: Giugno_1880_Pagina_047.jpg

description: Bulletin of 1880/06/12
DOI: 10.13130/rd_unimi/ui9ecn/my8k8u
title: Giugno_1880_Pagina_048.jpg

description: Bulletin of 1880/06/12
DOI: 10.13130/rd_unimi/ui9ecn/tdcnw0
title: Giugno_1880_Pagina_049.jpg

description: Bulletin of 1880/06/13
DOI: 10.13130/rd_unimi/ui9ecn/iwqfwn
title: Giugno_1880_Pagina_050.jpg

description: Bulletin of 1880/06/13
DOI: 10.13130/rd_unimi/ui9ecn/egy3v2
title: Giugno_1880_Pagina_051.jpg

description: Bulletin of 1880/06/13
DOI: 10.13130/rd_unimi/ui9ecn/mfxnsd
title: Giugno_1880_Pagina_052.jpg

description: Bulletin of 1880/06/13
DOI: 10.15468/dl.xgtj98
title: Occurrence Download

description: A dataset containing 47 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lumbricillus viridis",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 47 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040422-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13130/rd_unimi/ui9ecn/sa62up
title: Giugno_1880_Pagina_053.jpg

description: Bulletin of 1880/06/14
DOI: 10.13130/rd_unimi/ui9ecn/bdpe8x
title: Giugno_1880_Pagina_054.jpg

description: Bulletin of 1880/06/14
DOI: 10.13130/rd_unimi/ui9ecn/jfx6ag
title: Giugno_1880_Pagina_055.jpg

description: Bulletin of 1880/06/14
DOI: 10.13130/rd_unimi/ui9ecn/okgpap
title: Giugno_1880_Pagina_056.jpg

description: Bulletin of 1880/06/14
DOI: 10.15468/dl.vqazwz
title: Occurrence Download

description: A dataset listing the 2 species recorded in GBIF matching the query:
{
 "and" : [
 "Continent is Europe",
 "OccurrenceStatus is Present",
 "TaxonKey is Celastrus orbiculatus"
 ]
}

 The dataset's 2 records were derived from 67 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040407-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13130/rd_unimi/ui9ecn/5jeegj
title: Giugno_1880_Pagina_057.jpg

description: Bulletin of 1880/06/15
DOI: 10.13130/rd_unimi/ui9ecn/m76lov
title: Giugno_1880_Pagina_058.jpg

description: Bulletin of 1880/06/15
DOI: 10.13130/rd_unimi/ui9ecn/ie1f3b
title: Giugno_1880_Pagina_059.jpg

description: Bulletin of 1880/06/15
DOI: 10.13130/rd_unimi/ui9ecn/9yisxj
title: Giugno_1880_Pagina_060.jpg

description: Bulletin of 1880/06/15
DOI: 10.6084/m9.figshare.28739250
title: MagneticPillars++: Efficient LiDAR Odometry Via Deep Frame-To-Keyframe Point Cloud Registration

description: Downstream applications for point cloud registration, like LiDAR Odometry, often conduct Iterative Closest Points (ICP) in the initial frame-to-frame matching and/or subsequent map refinement. However, due to its distance-based processing nature, ICP relies on an accurate pose initialization while implicating increased computational complexity with a growing number of points. To meet specific runtime requirements, methods often apply the extensive mapping step at low frequencies, e.g. every 10 frames, which in turn leads to increased noise on the calculated trajectory. To tackle the discrepancy between runtime and accuracy, we present MagneticPillars++, an extension of our previous point cloud registration approach optimized for LiDAR Odometry, introducing novel intermediate cell correspondence filtering and accelerated match normalization. Furthermore, we propose a frame-to-keyframe matching technique replacing the simple frame-to-frame matching within a LiDAR Odometry pipeline. This can tremendously reduce noise without the need for expensive ICP corrections. We conduct extensive experiments for various tasks like point cloud registration, LiDAR Odometry, and loop closure estimation, demonstrating the versatility of our approach, where we are able to outperform state-of-the-art approaches in terms of accuracy and runtime, resulting in residual translation and rotation errors of up to <b>4.7 cm</b> and <b>0.231</b> with an average runtime of.
DOI: 10.6084/m9.figshare.28777336
title: Few-Shot Learning for Triplet-Based EV Energy Consumption Estimation

description: Predicting the energy consumption of an electric vehicle (EV) is often relevant when planning and managing electric mobility. The prediction is challenging as EV energy consumption is highly variable and dependent on <i>context</i>. First, this paper proposes an integrated framework for the collection of online telematic data, processing of this data, online maintenance of statistics, and machine-learning-based prediction of travel time and energy consumption. A key feature of the proposed framework is the preprocessing of the trajectory data into <i>triplets</i>, a convenient data unit that <i>captures the relevant context</i> necessary for effective energ y prediction. The second contribution of the paper addresses the effective <i>management of drastic change in context</i> through robust energy prediction models. In particular, using <i>few-shot</i> learning techniques, we tackle the problem of the need to create different energy prediction models for different EV types, from small EVs to electric buses. Experimental results on three different data sets demonstrate how energy prediction models adapt to different EV types.
DOI: 10.6084/m9.figshare.28777336.v1
title: Few-Shot Learning for Triplet-Based EV Energy Consumption Estimation

description: Predicting the energy consumption of an electric vehicle (EV) is often relevant when planning and managing electric mobility. The prediction is challenging as EV energy consumption is highly variable and dependent on <i>context</i>. First, this paper proposes an integrated framework for the collection of online telematic data, processing of this data, online maintenance of statistics, and machine-learning-based prediction of travel time and energy consumption. A key feature of the proposed framework is the preprocessing of the trajectory data into <i>triplets</i>, a convenient data unit that <i>captures the relevant context</i> necessary for effective energ y prediction. The second contribution of the paper addresses the effective <i>management of drastic change in context</i> through robust energy prediction models. In particular, using <i>few-shot</i> learning techniques, we tackle the problem of the need to create different energy prediction models for different EV types, from small EVs to electric buses. Experimental results on three different data sets demonstrate how energy prediction models adapt to different EV types.
DOI: 10.13130/rd_unimi/ui9ecn/enl3my
title: Giugno_1880_Pagina_061.jpg

description: Bulletin of 1880/06/16
DOI: 10.6084/m9.figshare.28739250.v1
title: MagneticPillars++: Efficient LiDAR Odometry Via Deep Frame-To-Keyframe Point Cloud Registration

description: Downstream applications for point cloud registration, like LiDAR Odometry, often conduct Iterative Closest Points (ICP) in the initial frame-to-frame matching and/or subsequent map refinement. However, due to its distance-based processing nature, ICP relies on an accurate pose initialization while implicating increased computational complexity with a growing number of points. To meet specific runtime requirements, methods often apply the extensive mapping step at low frequencies, e.g. every 10 frames, which in turn leads to increased noise on the calculated trajectory. To tackle the discrepancy between runtime and accuracy, we present MagneticPillars++, an extension of our previous point cloud registration approach optimized for LiDAR Odometry, introducing novel intermediate cell correspondence filtering and accelerated match normalization. Furthermore, we propose a frame-to-keyframe matching technique replacing the simple frame-to-frame matching within a LiDAR Odometry pipeline. This can tremendously reduce noise without the need for expensive ICP corrections. We conduct extensive experiments for various tasks like point cloud registration, LiDAR Odometry, and loop closure estimation, demonstrating the versatility of our approach, where we are able to outperform state-of-the-art approaches in terms of accuracy and runtime, resulting in residual translation and rotation errors of up to <b>4.7 cm</b> and <b>0.231</b> with an average runtime of.
DOI: 10.13130/rd_unimi/ui9ecn/vbx2lx
title: Giugno_1880_Pagina_062.jpg

description: Bulletin of 1880/06/16
DOI: 10.6084/m9.figshare.28262841
title: From Baseline to Best Practice: An Advanced Feature Selection, Feature Resampling and Grid Search Techniques to Improve Injury Severity Prediction

description: This work addresses the need for precise prediction models that predict the severity of injuries sustained in traffic crashes as a regression task. To this end, we thoroughly analyzed traffic crashes in Rome between 2016 and 2019, gathering data on vehicle attributes and environmental factors. Fourth predictive systems are employed to investigate the intricate problem of predicting the severity of injuries sustained in traffic crashes using different regression algorithms, such as Random Forest, Decision Trees, XGBoost, and Artificial Neural Networks. Compared to comparable systems without feature selection, feature resampling, and optimization methods, the results demonstrate that employing optimized XGBoost along with grid search in conjunction with SelectKBest and SMOTE strategy has resulted in greater performance, with an 89% R2 score. Our findings provide insight into the requirement for accurate forecasting models in optimization and balanced approaches to enhancing traffic safety. These findings offer a viable way to improve traffic safety tactics. As far as we know and as of right now, there hasn’t been much interest in supporting a fusion-based system that critically reviews machine learning techniques using grid search optimization, feature selection, and smote technique and examines how injury severity prediction is affected by road crashes.
DOI: 10.6084/m9.figshare.28262841.v1
title: From Baseline to Best Practice: An Advanced Feature Selection, Feature Resampling and Grid Search Techniques to Improve Injury Severity Prediction

description: This work addresses the need for precise prediction models that predict the severity of injuries sustained in traffic crashes as a regression task. To this end, we thoroughly analyzed traffic crashes in Rome between 2016 and 2019, gathering data on vehicle attributes and environmental factors. Fourth predictive systems are employed to investigate the intricate problem of predicting the severity of injuries sustained in traffic crashes using different regression algorithms, such as Random Forest, Decision Trees, XGBoost, and Artificial Neural Networks. Compared to comparable systems without feature selection, feature resampling, and optimization methods, the results demonstrate that employing optimized XGBoost along with grid search in conjunction with SelectKBest and SMOTE strategy has resulted in greater performance, with an 89% R2 score. Our findings provide insight into the requirement for accurate forecasting models in optimization and balanced approaches to enhancing traffic safety. These findings offer a viable way to improve traffic safety tactics. As far as we know and as of right now, there hasn’t been much interest in supporting a fusion-based system that critically reviews machine learning techniques using grid search optimization, feature selection, and smote technique and examines how injury severity prediction is affected by road crashes.
DOI: 10.6084/m9.figshare.28613646
title: MSNet: A Novel Deep Learning Framework for Efficient Missing Seedling Detection in Maize Fields

description: Machine vision application in agriculture has spurred significant interest in crop-missing detection. This study targets critical challenges, such as comprehensive aerial imagery coverage, tiny seedlings easily mistaken for weeds, and the absence of adaptive learning in traditional row classification. We propose a novel methodology for missing seedling detection, which comprises three essential stages: seedling localization, row classification, and missing region prediction. We present SeedNet, a detector that leverages row direction information to enhance small seedling detection performance. By incorporating this information, SeedNet improves recall by 25.3% and AP by 15.4% compared to the baseline. Additionally, we introduce PeakNet, a deep learning-based classifier for row segmentation that efficiently adapts to row spacing without any prior assumptions, achieving an accuracy of 99.69%. Finally, missing areas are predicted by analyzing the relative distances between adjacent seedlings within the same row. Under challenging outdoor conditions, this method achieves a missing detection accuracy of 98%, meeting practical requirements for field testing. SeedNet and PeakNet demonstrate exceptional performance in real-time processing, achieving inference speeds of 105 FPS and 2295 FPS, respectively. These results indicate strong potential for practical applications in real-time agricultural systems. The proposed approach provides a high-performance, low-cost solution for crop missing detection.
DOI: 10.6084/m9.figshare.28613646.v1
title: MSNet: A Novel Deep Learning Framework for Efficient Missing Seedling Detection in Maize Fields

description: Machine vision application in agriculture has spurred significant interest in crop-missing detection. This study targets critical challenges, such as comprehensive aerial imagery coverage, tiny seedlings easily mistaken for weeds, and the absence of adaptive learning in traditional row classification. We propose a novel methodology for missing seedling detection, which comprises three essential stages: seedling localization, row classification, and missing region prediction. We present SeedNet, a detector that leverages row direction information to enhance small seedling detection performance. By incorporating this information, SeedNet improves recall by 25.3% and AP by 15.4% compared to the baseline. Additionally, we introduce PeakNet, a deep learning-based classifier for row segmentation that efficiently adapts to row spacing without any prior assumptions, achieving an accuracy of 99.69%. Finally, missing areas are predicted by analyzing the relative distances between adjacent seedlings within the same row. Under challenging outdoor conditions, this method achieves a missing detection accuracy of 98%, meeting practical requirements for field testing. SeedNet and PeakNet demonstrate exceptional performance in real-time processing, achieving inference speeds of 105 FPS and 2295 FPS, respectively. These results indicate strong potential for practical applications in real-time agricultural systems. The proposed approach provides a high-performance, low-cost solution for crop missing detection.
DOI: 10.13130/rd_unimi/ui9ecn/ucp0l4
title: Giugno_1880_Pagina_063.jpg

description: Bulletin of 1880/06/16
DOI: 10.13130/rd_unimi/ui9ecn/y6djin
title: Giugno_1880_Pagina_064.jpg

description: Bulletin of 1880/06/16
DOI: 10.13130/rd_unimi/ui9ecn/fg4435
title: Giugno_1880_Pagina_065.jpg

description: Bulletin of 1880/06/17
DOI: 10.13130/rd_unimi/ui9ecn/srol6q
title: Giugno_1880_Pagina_066.jpg

description: Bulletin of 1880/06/17
DOI: 10.13130/rd_unimi/ui9ecn/1qfkdm
title: Giugno_1880_Pagina_067.jpg

description: Bulletin of 1880/06/17
DOI: 10.6084/m9.figshare.29212617
title: Augmentation of Semantic Processes for Deep Learning Applications

description: The popularity of Deep Learning (DL) methods used in business process management research and practice is constantly increasing. One important factor that hinders the adoption of DL in certain areas is the availability of sufficiently large training datasets, particularly affecting domains where process models are mainly defined manually with a high knowledge-acquisition effort. In this paper, we examine <i>process model augmentation</i> in combination with <i>semi-supervised transfer learning</i> to enlarge existing datasets and train DL models effectively. The use case of similarity learning between manufacturing process models is discussed. Based on a literature study of existing augmentation techniques, a concept is presented with different categories of augmentation from knowledge-light approaches to knowledge-intensive ones, e. g. based on automated planning. Specifically, the impacts of augmentation approaches on the syntactic and semantic correctness of the augmented process models are considered. The concept also proposes a semi-supervised transfer learning approach to integrate augmented and non-augmented process model datasets in a two-phased training procedure. The experimental evaluation investigates augmented process model datasets regarding their quality for model training in the context of similarity learning between manufacturing process models. The results indicate a large potential with a reduction of the prediction error of up to 53%.