cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.48366/r218943
title: Information theory-based DDoS Defence solutions in SDN

description: Information theory-based entropy and divergence metrics are widely used for DDoS attack detection. Entropy represents the randomness in the network features, whereas a divergence metric represents the similarity of two probability distributions. The concept of uncertainty’s measurement is coined initially by Claude Shannon in 1948. The information distance or divergence metric calculated using different probability distributions of traffic flows used to find the abnormality of network traffic. By using the entropy measure, it can be seen how the current network behaviour deviates from normal network behaviour, which leads to the detection of a DDoS attack. This is a comparison between the DDoS defence approaches using entropy and divergence metrics.
DOI: 10.23708/cblbhi
title: Microplastic concentrations in lagoon, bay and beach environments of Binh Dinh province, Central Vietnam. COMPOSE (2019-2021) and CounterMEASUREII (2021-2022) projects

description: Microplastic (300-5000µm) concentrations were measured in the Thi Nai lagoon, Quy Nhon bay and beach, Ha Tay River, Kon River, Cat Tien beach and Eo beach environments of Binh Dinh province, Central Vietnam under the COMPOSE project (2019-2021) and the CounterMEASUREII project (2021-2022). Microplastic were sampled using a 80µm mesh size net from a local boat. The samples were extracted in the laboratory using enzymatic digestion, NaCl density separation and were filtrated on GF-A filter. Then microplastic were observed on stereomicroscope and length, area, color were measured for fragments, line and circle. Concentrations are given in total microplastic per m3, fragment per m3 and fibers per m3. The sampling dates are October 2019, January 2020, April 2020, July 2020, October 2020, January 2021 and March 2021 under COMPOSE Project; September 2021, October 2021 and January 2022 under the CounterMEASUREII project.
DOI: 10.5281/zenodo.7117174
title: Santoraj/Manuscript-data: Final Release

description: These are the data of the research entitled "Diversity Assessment and Characterization of Rice (<em>Oryza sativa</em> L.) in Terai of Nepal.
DOI: 10.5281/zenodo.7117173
title: Santoraj/Manuscript-data: Final Release

description: These are the data of the research entitled "Diversity Assessment and Characterization of Rice (<em>Oryza sativa</em> L.) in Terai of Nepal.
DOI: 10.6084/m9.figshare.19382883
title: Isolation and characterisation of monoclonal picocyanobacterial strains from contrasting New Zealand lakes

description: Freshwater picocyanobacteria form the base of microbial food webs in many lakes worldwide but have received less attention than other phytoplankton. Little is known about their potential response to environmental changes such as increased nutrient loading and climate change, due partly to the lack of available cultured and sequenced strains. Here, we isolated 25 monoclonal picocyanobacterial strains from 6 New Zealand lakes with contrasting trophic states. The use of MLA medium instead of BG11 proved highly successful for the rapid isolation of picocyanobacteria. Strains were characterised by sequencing of the 16S ribosomal RNA gene, spectrophotometry, and high-performance liquid chromatography. 16S rRNA gene analysis placed most strains within the cluster 5 picocyanobacterial lineage (sub-cluster 5.2, family: Synechococcaceae). Phylogenetic analysis showed that 12 isolates from Lakes Wakatipu, Hayes, Johnson, and Ellesmere/Te Waihora clustered with strains from a range of Northern Hemisphere locations, suggesting global dispersal of these strains. Pigment characterisation revealed that pink and brown cultures from oligotrophic and some eutrophic lakes were rich in phycoerythrin, while green cultures from eutrophic and hypertrophic lakes were rich in phycocyanin. This diverse group of freshwater cluster 5 picocyanobacterial cultures will provide a new resource to study how these critically important microbes function and respond to changing environmental stressors.
DOI: 10.6084/m9.figshare.19382883.v2
title: Isolation and characterisation of monoclonal picocyanobacterial strains from contrasting New Zealand lakes

description: Freshwater picocyanobacteria form the base of microbial food webs in many lakes worldwide but have received less attention than other phytoplankton. Little is known about their potential response to environmental changes such as increased nutrient loading and climate change, due partly to the lack of available cultured and sequenced strains. Here, we isolated 25 monoclonal picocyanobacterial strains from 6 New Zealand lakes with contrasting trophic states. The use of MLA medium instead of BG11 proved highly successful for the rapid isolation of picocyanobacteria. Strains were characterised by sequencing of the 16S ribosomal RNA gene, spectrophotometry, and high-performance liquid chromatography. 16S rRNA gene analysis placed most strains within the cluster 5 picocyanobacterial lineage (sub-cluster 5.2, family: Synechococcaceae). Phylogenetic analysis showed that 12 isolates from Lakes Wakatipu, Hayes, Johnson, and Ellesmere/Te Waihora clustered with strains from a range of Northern Hemisphere locations, suggesting global dispersal of these strains. Pigment characterisation revealed that pink and brown cultures from oligotrophic and some eutrophic lakes were rich in phycoerythrin, while green cultures from eutrophic and hypertrophic lakes were rich in phycocyanin. This diverse group of freshwater cluster 5 picocyanobacterial cultures will provide a new resource to study how these critically important microbes function and respond to changing environmental stressors.
DOI: 10.6084/m9.figshare.19089959
title: Modelling how bottom-up and top-down processes control the major functional groups of biota in a large temperate shallow lake

description: Ecosystem models that measure the impact of quantitative interactions between trophic levels are widely used tools in ecosystem studies and fishery management. We constructed a mass-balance trophic model using an Ecopath with Ecosim (EwE) modelling suite for large shallow Lake Võrtsjärv, Estonia. The model was calibrated for 36 years (1983–2018) and included 23 functional groups. We examined trophic relationships, functional group interactions, energy fluxes, and keystone groups having a high impact on the ecosystem relative to their biomass. We tested 6 hypothetical scenarios based on future biomass changes for the major functional groups (phytoplankton, zooplankton, macrozoobenthos, piscivorous fish, and bream) for 20 years. The output of the predictive scenarios showed that the biomass changes of planktonic groups would affect the whole food web. Among consumers, macrozoobenthos was crucial for the food web balance because a reduction of their biomass would also reduce the biomass of the fish community. Changes in fish catches would cause minimal biomass difference in other groups. While increased fishing pressure on large piscivores would have a marked effect on the rest of the food web, the reduction of nonpiscivorous fish like bream would have little effect. The results suggested a positive relationship between the biomass of small phytoplankton and fish, alluding to the prevalence of bottom-up trophic processes. These outcomes could be helpful for assessing trophic dynamics in shallow lakes and important aspects for fisheries and ecosystem management.
DOI: 10.6084/m9.figshare.19089959.v2
title: Modelling how bottom-up and top-down processes control the major functional groups of biota in a large temperate shallow lake

description: Ecosystem models that measure the impact of quantitative interactions between trophic levels are widely used tools in ecosystem studies and fishery management. We constructed a mass-balance trophic model using an Ecopath with Ecosim (EwE) modelling suite for large shallow Lake Võrtsjärv, Estonia. The model was calibrated for 36 years (1983–2018) and included 23 functional groups. We examined trophic relationships, functional group interactions, energy fluxes, and keystone groups having a high impact on the ecosystem relative to their biomass. We tested 6 hypothetical scenarios based on future biomass changes for the major functional groups (phytoplankton, zooplankton, macrozoobenthos, piscivorous fish, and bream) for 20 years. The output of the predictive scenarios showed that the biomass changes of planktonic groups would affect the whole food web. Among consumers, macrozoobenthos was crucial for the food web balance because a reduction of their biomass would also reduce the biomass of the fish community. Changes in fish catches would cause minimal biomass difference in other groups. While increased fishing pressure on large piscivores would have a marked effect on the rest of the food web, the reduction of nonpiscivorous fish like bream would have little effect. The results suggested a positive relationship between the biomass of small phytoplankton and fish, alluding to the prevalence of bottom-up trophic processes. These outcomes could be helpful for assessing trophic dynamics in shallow lakes and important aspects for fisheries and ecosystem management.
DOI: 10.21227/bcnz-s159
title: Two-stage robust coordination optimization strategy of data and power in a data center park

description: fit for Two-stage robust coordination optimization strategy of data and power in a data center park
DOI: 10.6073/pasta/6b5bd9b8ef282fb9a23ba89572835f68
title: MCR LTER: Coral Reef: Long-term Population and Community Dynamics: Other Benthic Invertebrates, ongoing since 2005

description: The data presented here are the abundances of the major invertebrate herbivores and corallivores on Moorea coral reefs. Abundances are estimated in 4 fixed quadrats along 5 permanent transects at each of 4 habitats at 2 sites on each of the 3 shores of Moorea each year. Counts are made in one-meter-squared quadrats. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2020). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.
DOI: 10.5281/zenodo.7117162
title: Data for testing the SIAMCAT R package

description: Datasets needed for the vignettes of the SIAMCAT R package
DOI: 10.5281/zenodo.7117161
title: Data for testing the SIAMCAT R package

description: Datasets needed for the vignettes of the SIAMCAT R package
DOI: 10.25318/36280001202200900002-eng
title: Postsecondary education decisions following a permanent layoff

description: Experiencing a permanent layoff—a job loss without returning to the same employer during the same or subsequent year—can have significant and long-lasting impacts. One strategy to cope with job loss is to retrain. However, until recently, data limitations have prevented researchers from observing the detailed training activities of Canadians who have been permanently laid off. This study aims to address this gap by documenting the detailed postsecondary training decisions made by affected workers following job displacement.
DOI: 10.25318/36280001202200900002-fra
title: Décisions d’entreprendre des études postsecondaires à la suite d’une mise à pied permanente

description: Le fait de subir une mise à pied permanente, c’est-à-dire une perte d’emploi qui n’est pas suivie d’un retour de l’employé avec le même employeur au cours de la même année ou de l’année suivante, peut avoir des répercussions importantes et durables. L’une des stratégies utilisées pour faire face à la perte d’un emploi est de se recycler sur le plan professionnel. Toutefois, jusqu’à récemment, les limites des données ont empêché les chercheurs de se pencher sur les activités de formation détaillées des Canadiens mis à pied de façon permanente. Cette étude vise à combler cette lacune en consignant les décisions détaillées en matière de formation postsecondaire prises par les travailleurs en question après par une suppression d’emploi.
DOI: 10.5281/zenodo.7065147
title: SYNTHETIC VOLUMES AND 3D GROUND TRUTH ANNOTATIONS OF NUCLEI IN 3D MICROSCOPY VOLUMES

description: Manual ground truth annotations of subvolumes of eight microscopy volumes are provided. Synthetic subvolumes, generated with an SpCycelGAN, of the same volumes are also provided.
DOI: 10.5281/zenodo.7065146
title: SYNTHETIC VOLUMES AND 3D GROUND TRUTH ANNOTATIONS OF NUCLEI IN 3D MICROSCOPY VOLUMES

description: Manual ground truth annotations of subvolumes of eight microscopy volumes are provided. Synthetic subvolumes, generated with an SpCycelGAN, of the same volumes are also provided.
DOI: 10.48366/r218989
title: Machine learning-based DDoS defense solutions in SDN

description: Machine learning algorithms are used to solve complex problems in many fields. These algorithms are also applied for detection of DDoS attacks, and it has been found that they are better than signature-based detection techniques. These ML-based classifiers can be trained to determine the abnormal behaviour of the network traffic with more accuracy. Some commonly used classifiers based on machine learning are Support Vector Machine (SVM), Hidden Markov Model (HMM), Decision Tree (J48), Naive Bayes, Logistic regression, Random Trees, Binary Bat algorithm, Random forest, and K-nearest neighbour (KNN). This is a comparison of Machine learning approaces for DDoS attack detection.
DOI: 10.5281/zenodo.7115391
title: Microssatelite CPD

description: Compilação em DataFrame do número de pares de bases de diferentes animais (linhas) para 17 marcadores de microsatélite (coluna) de animais da raça Curraleiro Pé-Duro, uma raça de bovinos nativos do Brasil.
DOI: 10.5281/zenodo.7115390
title: Microssatelite CPD

description: Compilação em DataFrame do número de pares de bases de diferentes animais (linhas) para 17 marcadores de microsatélite (coluna) de animais da raça Curraleiro Pé-Duro, uma raça de bovinos nativos do Brasil.
DOI: 10.6084/m9.figshare.20362224
title: Template Independent Component Analysis with Spatial Priors for Accurate Subject-Level Brain Network Estimation and Inference

description: Independent component analysis is commonly applied to functional magnetic resonance imaging (fMRI) data to extract independent components (ICs) representing functional brain networks. While ICA produces reliable group-level estimates, single-subject ICA often produces noisy results. Template ICA is a hierarchical ICA model using empirical population priors to produce more reliable subject-level estimates. However, this and other hierarchical ICA models assume unrealistically that subject effects are spatially independent. Here, we propose spatial template ICA (stICA), which incorporates spatial priors into the template ICA framework for greater estimation efficiency. Additionally, the joint posterior distribution can be used to identify brain regions engaged in each network using an excursions set approach. By leveraging spatial dependencies and avoiding massive multiple comparisons, stICA has high power to detect true effects. We derive an efficient expectation-maximization algorithm to obtain maximum likelihood estimates of the model parameters and posterior moments of the latent fields. Based on analysis of simulated data and fMRI data from the Human Connectome Project, we find that stICA produces estimates that are more accurate and reliable than benchmark approaches, and identifies larger and more reliable areas of engagement. The algorithm is computationally tractable, achieving convergence within 12 hr for whole-cortex fMRI analysis. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.20362224.v1
title: Template independent component analysis with spatial priors for accurate subject-level brain network estimation and inference

description: Independent component analysis is commonly applied to functional magnetic resonance imaging (fMRI) data to extract independent components (ICs) representing functional brain networks. While ICA produces reliable group-level estimates, single-subject ICA often produces noisy results. Template ICA is a hierarchical ICA model using empirical population priors to produce more reliable subject-level estimates. However, this and other hierarchical ICA models assume unrealistically that subject effects are spatially independent. Here, we propose spatial template ICA (stICA), which incorporates spatial priors into the template ICA framework for greater estimation efficiency. Additionally, the joint posterior distribution can be used to identify brain regions engaged in each network using an excursions set approach. By leveraging spatial dependencies and avoiding massive multiple comparisons, stICA has high power to detect true effects. We derive an efficient expectation-maximization algorithm to obtain maximum likelihood estimates of the model parameters and posterior moments of the latent fields. Based on analysis of simulated data and fMRI data from the Human Connectome Project, we find that stICA produces estimates that are more accurate and reliable than benchmark approaches, and identifies larger and more reliable areas of engagement. The algorithm is computationally tractable, achieving convergence within 12 hours for whole-cortex fMRI analysis.
DOI: 10.5517/ccdc.csd.cc294yt7
title: CCDC 2119929: Experimental Crystal Structure Determination

description: Related Article: Masahiro Ikeshita, Sho Furukawa, Takahiro Ishikawa, Kana Matsudaira, Yoshitane Imai, Takashi Tsuno|2022|ChemistryOpen|11|e202100277|doi:10.1002/open.202100277
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: 10.5517/ccdc.csd.cc296vp2
title: CCDC 2121754: Experimental Crystal Structure Determination

description: Related Article: Ya Liu, Lianshe Fu, Gui-Ying Dong|2022|Inorg.Chim.Acta|540|121064|doi:10.1016/j.ica.2022.121064
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: 10.5517/ccdc.csd.cc296vv7
title: CCDC 2121759: Experimental Crystal Structure Determination

description: Related Article: Ya Liu, Lianshe Fu, Gui-Ying Dong|2022|Inorg.Chim.Acta|540|121064|doi:10.1016/j.ica.2022.121064
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: 10.6084/m9.figshare.20362222
title: Micro–Macro Changepoint Inference for Periodic Data Sequences

description: Existing changepoint approaches consider changepoints to occur linearly in time; one changepoint happens after another and they are not linked. However, data processes may have regularly occurring changepoints, for example, a yearly increase in sales of ice-cream on the first hot weekend. Using linear changepoint approaches here will miss more global features such as a decrease in sales of ice-cream due to other product availability. Being able to tease these global changepoint features from the more local (periodic) ones is beneficial for inference. We propose a periodic changepoint model to model this behavior using a mixture of a periodic and linear time perspective. Built around a Reversible Jump Markov chain Monte Carlo sampler, the Bayesian framework is used to study the local (periodic) changepoint behavior. To identify the optimal global changepoint positions we integrate the local changepoint model into the pruned exact linear time (PELT) search algorithm. We demonstrate that the method detects both local and global changepoints with high accuracy on simulated and motivating applications that share periodic behavior. Due to the micro–macro nature of the analysis, visualization of the results can be challenging. We additionally provide a unique perspective for changepoint visualizations in these data sequences. Supplementary Materials for this article are available online.
DOI: 10.6084/m9.figshare.20362222.v2
title: Micro–Macro Changepoint Inference for Periodic Data Sequences

description: Existing changepoint approaches consider changepoints to occur linearly in time; one changepoint happens after another and they are not linked. However, data processes may have regularly occurring changepoints, for example, a yearly increase in sales of ice-cream on the first hot weekend. Using linear changepoint approaches here will miss more global features such as a decrease in sales of ice-cream due to other product availability. Being able to tease these global changepoint features from the more local (periodic) ones is beneficial for inference. We propose a periodic changepoint model to model this behavior using a mixture of a periodic and linear time perspective. Built around a Reversible Jump Markov chain Monte Carlo sampler, the Bayesian framework is used to study the local (periodic) changepoint behavior. To identify the optimal global changepoint positions we integrate the local changepoint model into the pruned exact linear time (PELT) search algorithm. We demonstrate that the method detects both local and global changepoints with high accuracy on simulated and motivating applications that share periodic behavior. Due to the micro–macro nature of the analysis, visualization of the results can be challenging. We additionally provide a unique perspective for changepoint visualizations in these data sequences. Supplementary Materials for this article are available online.
DOI: 10.6084/m9.figshare.20362222.v1
title: Micro-Macro Changepoint Inference for Periodic Data Sequences

description: Existing changepoint approaches consider changepoints to occur linearly in time; one changepoint happens after another and they are not linked. However, data processes may have regularly occurring changepoints, e.g. a yearly increase in sales of ice-cream on the first hot weekend. Using linear changepoint approaches here will miss more global features such as a decrease in sales of ice-cream due to other product availability. Being able to tease these global changepoint features from the more local (periodic) ones is beneficial for inference. We propose a periodic changepoint model to model this behavior using a mixture of a periodic and linear time perspective. Built around a Reversible Jump Markov Chain Monte Carlo sampler, the Bayesian framework is used to study the local (periodic) changepoint behavior. To identify the optimal global changepoint positions we integrate the local changepoint model into the pruned exact linear time (PELT) search algorithm. We demonstrate that the method detects both local and global changepoints with high accuracy on simulated and motivating applications that share periodic behavior. Due to the micro-macro nature of the analysis, visualization of the results can be challenging. We additionally provide a unique perspective for changepoint visualizations in these data sequences. Supplementary Materials for this article are available online.