cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh2839318.08fu
title: SH2839318.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh2839319.08fu
title: SH2839319.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1862258.08fu
title: SH1862258.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1862259.08fu
title: SH1862259.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh2839320.08fu
title: SH2839320.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh2839321.08fu
title: SH2839321.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh2839322.08fu
title: SH2839322.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.25440/smu.17040326
title: Data from: A large-scale benchmark for food image segmentation

description: Food image segmentation is a critical and indispensible task for developing health-related applications such as estimating food calories and nutrients. Existing food image segmentation models are underperforming due to two reasons: (1) there is a lack of high quality food image datasets with fine-grained ingredient labels and pixel-wise location masks---the existing datasets either carry coarse ingredient labels or are small in size; and (2) the complex appearance of food makes it difficult to localize and recognize ingredients in food images, e.g., the ingredients may overlap one another in the same image, and the identical ingredient may appear distinctly in different food images.In this work, we build a new food image dataset FoodSeg103 (and its extension FoodSeg154) containing 9,490 images. We annotate these images with 154 ingredient classes and result in an average of 6 ingredient labels and pixel-wise masks per image. In addition, we propose a multi-modality pre-training approach called ReLeM that explicitly equips the model with rich and semantic food knowledge. In experiments, we use three popular semantic segmentation methods (i.e., Dilated Convolution based, Feature Pyramid based, and Vision Transformer based) as baselines, and evaluate them as well as ReLeM on our new datasets. We believe that the FoodSeg103 (and its extension FoodSeg154) and the pre-trained models using ReLeM can serve as a benchmark to facilitate future works in fine-grained food image understanding.<br>
DOI: 10.25440/smu.17040326.v1
title: Data from: A large-scale benchmark for food image segmentation

description: Food image segmentation is a critical and indispensible task for developing health-related applications such as estimating food calories and nutrients. Existing food image segmentation models are underperforming due to two reasons: (1) there is a lack of high quality food image datasets with fine-grained ingredient labels and pixel-wise location masks---the existing datasets either carry coarse ingredient labels or are small in size; and (2) the complex appearance of food makes it difficult to localize and recognize ingredients in food images, e.g., the ingredients may overlap one another in the same image, and the identical ingredient may appear distinctly in different food images.In this work, we build a new food image dataset FoodSeg103 (and its extension FoodSeg154) containing 9,490 images. We annotate these images with 154 ingredient classes and result in an average of 6 ingredient labels and pixel-wise masks per image. In addition, we propose a multi-modality pre-training approach called ReLeM that explicitly equips the model with rich and semantic food knowledge. In experiments, we use three popular semantic segmentation methods (i.e., Dilated Convolution based, Feature Pyramid based, and Vision Transformer based) as baselines, and evaluate them as well as ReLeM on our new datasets. We believe that the FoodSeg103 (and its extension FoodSeg154) and the pre-trained models using ReLeM can serve as a benchmark to facilitate future works in fine-grained food image understanding.<br>
DOI: 10.25440/smu.17039693
title: Data from: Disentangling hate in online memes

description: This is the data and code for "Disentangling Hate in Online Memes", published in Proceedings of the 29th ACMInternational Conference on Multimedia (MM ’21), October 20–24, 2021, Virtual Event, China. https://doi.org/10.1145/3474085.3475625<br>The project <i>DisMultiHate</i> is published on GitLab with data, code and documentation to reproduce the analysis. <br>
DOI: 10.25440/smu.17039693.v1
title: Data from: Disentangling hate in online memes

description: This is the data and code for "Disentangling Hate in Online Memes", published in Proceedings of the 29th ACMInternational Conference on Multimedia (MM ’21), October 20–24, 2021, Virtual Event, China. https://doi.org/10.1145/3474085.3475625<br>The project <i>DisMultiHate</i> is published on GitLab with data, code and documentation to reproduce the analysis. <br>
DOI: 10.3886/e116186v1-102800
title: Replication data for: The Economic Consequences of Hospital Admissions

description: We use an event study approach to examine the economic consequences of hospital admissions for adults in two datasets: survey data from the Health and Retirement Study, and hospitalization data linked to credit reports. For non-elderly adults with health insurance, hospital admissions increase out-of-pocket medical spending, unpaid medical bills, and bankruptcy, and reduce earnings, income, access to credit, and consumer borrowing. The earnings decline is substantial compared to the out-of-pocket spending increase, and is minimally insured prior to age-eligibility for Social Security Retirement Income. Relative to the insured non-elderly, the uninsured non-elderly experience much larger increases in unpaid medical bills and bankruptcy rates following a hospital admission. Hospital admissions trigger fewer than 5 percent of all bankruptcies in our sample.
DOI: 10.6084/m9.figshare.17097179
title: Figure 3.xlsx

description: The growth and reproduction of <i>Daphnia</i> raised on <i>Daphnia</i> (Chlo) and bacteria converted soil DOC with DOC:Chlo ratios of 1:2, 1:1, and 2:1. The treatment of <i>Daphnia</i> fed exclusively with <i>Chlorella</i> or DOC is set as a positive control and a negative control, respectively. Body length (mm, mean ± SD) (a), cumulative neonates (ind<sup>-1</sup>, mean ± SD) (b), survival rate (%, mean ± SD) (c), and intrinsic rates of population increase (d<sup>-1</sup>).
DOI: 10.6084/m9.figshare.17097179.v1
title: Figure 3.xlsx

description: The growth and reproduction of <i>Daphnia</i> raised on <i>Daphnia</i> (Chlo) and bacteria converted soil DOC with DOC:Chlo ratios of 1:2, 1:1, and 2:1. The treatment of <i>Daphnia</i> fed exclusively with <i>Chlorella</i> or DOC is set as a positive control and a negative control, respectively. Body length (mm, mean ± SD) (a), cumulative neonates (ind<sup>-1</sup>, mean ± SD) (b), survival rate (%, mean ± SD) (c), and intrinsic rates of population increase (d<sup>-1</sup>).
DOI: 10.6084/m9.figshare.17067365
title: supplementary tables

description: Supplementary tables of a paper titled "A new early Miocene archaic dolphin (Odontoceti, Cetacea) from New Zealand, and brain evolution of the Odontoceti"
DOI: 10.6084/m9.figshare.17067365.v3
title: supplementary tables

description: Supplementary tables of a paper titled "A new early Miocene archaic dolphin (Odontoceti, Cetacea) from New Zealand, and brain evolution of the Odontoceti"
DOI: 10.6084/m9.figshare.17067365.v2
title: supplementary tables

description: Supplementary tables of a paper titled "A new early Miocene archaic dolphin (Odontoceti, Cetacea) from New Zealand, and brain evolution of the Odontoceti"
DOI: 10.17909/gzd9-1n79
title: NGC5846-UDG1 HST images
DOI: 10.5281/zenodo.5739349
title: Measurement report: The importance of biomass burning in light extinction and direct radiative effect of urban aerosol during the COVID-19 lockdown in China

description: To explore the impacts of anthropogenic emissions on aerosol optical properties and direct radiative effect (DRE), a series of real-time measurements was conducted in an urban area of China before and during the lockdown of Coronavirus Disease 2019. The generalized addictive model analysis shows that the light extinction coefficient (<em>b</em><sub>ext</sub>) decreased largely during the lockdown due to the sharp reductions in anthropogenic emissions. Organic aerosol was the largest contributor to <em>b</em><sub>ext</sub> based on the ridge regression analysis. A hybrid environmental receptor model combining with chemical and optical variables shows that <em>b</em><sub>ext</sub> from biomass burning increased in the lockdown due to the undiminished needs of residential cooking and heating in winter. Biomass burning contributed the largest positive effect to aerosol DRE in the atmosphere during the lockdown, highlighting the importance of controlling biomass burning for mitigating climate change in China in the future.
DOI: 10.25345/c5b28b
title: MassIVE MSV000088473 - 20211130_LC-MS_dataFiles_ChineseDarkTeas_Fungi
title: Chinese dark teas (CDTs) are traditional fermented beverages. These LC-MS data helps to know the chemical diversities of CDTs, and the metabolites shared between CDTs and fungi are investigated.
DOI: 10.5281/zenodo.5558592
title: Data from: Direct visualization of a static incommensurate antiferromagnetic order in Fe-doped Bi2Sr2CaCu2O8+δ

description: This database contains all the necessary data of the manuscript. <strong>Abstract of the manuscript</strong> In cuprate superconductors, due to strong electronic correlations, there are multiple intertwined orders which either coexist or compete with superconductivity. Among them, the antiferromagnetic (AF) order is the most prominent one. In the region where superconductivity sets in, the long-range AF order is destroyed. Yet the residual short-range AF spin fluctuations are present up to a much higher doping, and their role in the emergence of the superconducting phase is still highly debated. Here, by using a spin-polarized scanning tunneling microscope, we directly visualize an emergent incommensurate AF order in the nearby region of Fe impurities embedded in the optimally doped Bi<sub>2</sub>Sr<sub>2</sub>CaCu<sub>2</sub>O<sub>8+δ</sub> (Bi2212). Remarkably, the Fe impurities suppress the superconducting coherence peaks with the gapped feature intact, but pin down the ubiquitous short-range incommensurate AF order. Our work shows an intimate relation between antiferromagnetism and superconductivity.
DOI: 10.5281/zenodo.5733923
title: A study of generalization and compatibility performance of 3D U‑Net segmentation on multiple heterogeneous liver CT datasets

description: Background: Most existing algorithms have been focused on the segmentation from several public Liver CT<br> datasets scanned regularly (no pneumoperitoneum and horizontal supine position). This study primarily segmented<br> datasets with unconventional liver shapes and intensities deduced by contrast phases, irregular scanning conditions,<br> different scanning objects of pigs and patients with large pathological tumors, which formed the multiple heterogeneity of datasets used in this study.<br> Methods: The multiple heterogeneous datasets used in this paper includes: (1) One public contrast-enhanced CT<br> dataset and one public non-contrast CT dataset; (2) A contrast-enhanced dataset that has abnormal liver shape with<br> very long left liver lobes and large-sized liver tumors with abnormal presets deduced by microvascular invasion; (3)<br> One artificial pneumoperitoneum dataset under the pneumoperitoneum and three scanning profiles (horizontal/<br> left/right recumbent position); (4) Two porcine datasets of Bama type and domestic type that contains pneumoperitoneum cases but with large anatomy discrepancy with humans. The study aimed to investigate the segmentation performances of 3D U-Net in: (1) generalization ability between multiple heterogeneous datasets by cross-testing experiments; (2) the compatibility when hybrid training all datasets in different sampling and encoder layer sharing schema. We further investigated the compatibility of encoder level by setting separate level for each dataset (i.e., dataset-wise convolutions) while sharing the decoder.<br> Results: Model trained on different datasets has different segmentation performance. The prediction accuracy<br> between LiTS dataset and Zhujiang dataset was about 0.955 and 0.958 which shows their good generalization ability<br> due to that they were all contrast-enhanced clinical patient datasets scanned regularly. For the datasets scanned<br> under pneumoperitoneum, their corresponding datasets scanned without pneumoperitoneum showed good generalization ability. Dataset-wise convolution module in high-level can improve the dataset unbalance problem. The experimental results will facilitate researchers making solutions when segmenting those special datasets. Conclusions: (1) Regularly scanned datasets is well generalized to irregularly ones. (2) The hybrid training is beneficial but the dataset imbalance problem always exits due to the multi-domain homogeneity. The higher levels<br> encoded more domain specific information than lower levels and thus were less compatible in terms of our datasets.
description: {"references": ["Baochun He, et al. A study of generalization and compatibility performance of 3D U\u2011Net segmentation on multiple heterogeneous liver CT datasets. BMC Medical Imaging. 21: 178. 2021."]}
DOI: 10.5281/zenodo.5733922
title: A study of generalization and compatibility performance of 3D U‑Net segmentation on multiple heterogeneous liver CT datasets

description: Background: Most existing algorithms have been focused on the segmentation from several public Liver CT<br> datasets scanned regularly (no pneumoperitoneum and horizontal supine position). This study primarily segmented<br> datasets with unconventional liver shapes and intensities deduced by contrast phases, irregular scanning conditions,<br> different scanning objects of pigs and patients with large pathological tumors, which formed the multiple heterogeneity of datasets used in this study.<br> Methods: The multiple heterogeneous datasets used in this paper includes: (1) One public contrast-enhanced CT<br> dataset and one public non-contrast CT dataset; (2) A contrast-enhanced dataset that has abnormal liver shape with<br> very long left liver lobes and large-sized liver tumors with abnormal presets deduced by microvascular invasion; (3)<br> One artificial pneumoperitoneum dataset under the pneumoperitoneum and three scanning profiles (horizontal/<br> left/right recumbent position); (4) Two porcine datasets of Bama type and domestic type that contains pneumoperitoneum cases but with large anatomy discrepancy with humans. The study aimed to investigate the segmentation performances of 3D U-Net in: (1) generalization ability between multiple heterogeneous datasets by cross-testing experiments; (2) the compatibility when hybrid training all datasets in different sampling and encoder layer sharing schema. We further investigated the compatibility of encoder level by setting separate level for each dataset (i.e., dataset-wise convolutions) while sharing the decoder.<br> Results: Model trained on different datasets has different segmentation performance. The prediction accuracy<br> between LiTS dataset and Zhujiang dataset was about 0.955 and 0.958 which shows their good generalization ability<br> due to that they were all contrast-enhanced clinical patient datasets scanned regularly. For the datasets scanned<br> under pneumoperitoneum, their corresponding datasets scanned without pneumoperitoneum showed good generalization ability. Dataset-wise convolution module in high-level can improve the dataset unbalance problem. The experimental results will facilitate researchers making solutions when segmenting those special datasets. Conclusions: (1) Regularly scanned datasets is well generalized to irregularly ones. (2) The hybrid training is beneficial but the dataset imbalance problem always exits due to the multi-domain homogeneity. The higher levels<br> encoded more domain specific information than lower levels and thus were less compatible in terms of our datasets.
description: {"references": ["Baochun He, et al. A study of generalization and compatibility performance of 3D U\u2011Net segmentation on multiple heterogeneous liver CT datasets. BMC Medical Imaging. 21: 178. 2021."]}