cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.20652792
title: Additional file 1 of The spatial and temporal exploitation of anthropogenic food sources by common ravens (Corvus corax) in the Alps

description: Additional file 1. General information on the 81 individuals (ID, Name) that were tracked for 2.75 years in the Austrian Alps, including age class, season, sex (m = male, f = female), origin and death comment. The number of tracking days, fixes, the occurrence distribution (calculated as the 95% contour area of the utilization distribution or 95% UD), the average maximum daily displacements, the number of AFSs visited and the proportion of GPS fixes inside a buffer are presented for each individual-season-year combination (n = 376).
DOI: 10.6084/m9.figshare.20652792.v1
title: Additional file 1 of The spatial and temporal exploitation of anthropogenic food sources by common ravens (Corvus corax) in the Alps

description: Additional file 1. General information on the 81 individuals (ID, Name) that were tracked for 2.75 years in the Austrian Alps, including age class, season, sex (m = male, f = female), origin and death comment. The number of tracking days, fixes, the occurrence distribution (calculated as the 95% contour area of the utilization distribution or 95% UD), the average maximum daily displacements, the number of AFSs visited and the proportion of GPS fixes inside a buffer are presented for each individual-season-year combination (n = 376).
DOI: 10.6084/m9.figshare.20652798
title: Additional file 3 of The spatial and temporal exploitation of anthropogenic food sources by common ravens (Corvus corax) in the Alps

description: Additional file 3. Information on the 45 anthropogenic food sources. Broader resource type groupings (refuse, hut, game park) were based on similarities in sites that were verified (Y) and described from the field surveys. Buffers were estimated based on revisitation analysis, at the distance where revisitations no longer increased with increasing radius size or at the maximum of 150 metres (Additional file 4). The number of different individuals (No. Ind) that were recorded at a site across the tracking period is also reported.
DOI: 10.6084/m9.figshare.20652798.v1
title: Additional file 3 of The spatial and temporal exploitation of anthropogenic food sources by common ravens (Corvus corax) in the Alps

description: Additional file 3. Information on the 45 anthropogenic food sources. Broader resource type groupings (refuse, hut, game park) were based on similarities in sites that were verified (Y) and described from the field surveys. Buffers were estimated based on revisitation analysis, at the distance where revisitations no longer increased with increasing radius size or at the maximum of 150 metres (Additional file 4). The number of different individuals (No. Ind) that were recorded at a site across the tracking period is also reported.
DOI: 10.5281/zenodo.7023277
title: Model Zoo: A Dataset of Diverse Populations of Resnet-18 Models - Tiny ImageNet

description: <strong>Abstract</strong> In the last years, neural networks have evolved from laboratory environments to the state-of-the-art for many real-world problems. Our hypothesis is that neural network models (i.e., their weights and biases) evolve on unique, smooth trajectories in weight space during training. Following, a population of such neural network models (refereed to as “model zoo”) would form topological structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can be reveal latent properties of individual models. With such zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of neural network weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of neural networks. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of neural network models for further research. In total the proposed model zoo dataset is based on six image datasets, consist of 27 model zoos with varying hyperparameter combinations are generated and includes 50’360 unique neural network models resulting in over 2’585’360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks as mentioned before. <strong>Dataset</strong> This dataset is part of a larger collection of model zoos and contains the zoo of 1000 ResNet18 models trained on Tiny Imagenet. All zoos with extensive information and code can be found at www.modelzoos.cc. The complete zoo is 2.6TB large. Due to the size, this repository contains the checkpoints of the first 115 models at their last epoch 60. For a link to the full dataset as well as more information on the zoos and code to access and use the zoos, please see www.modelzoos.cc.
DOI: 10.5281/zenodo.7023278
title: Model Zoo: A Dataset of Diverse Populations of Resnet-18 Models - Tiny ImageNet

description: <strong>Abstract</strong> In the last years, neural networks have evolved from laboratory environments to the state-of-the-art for many real-world problems. Our hypothesis is that neural network models (i.e., their weights and biases) evolve on unique, smooth trajectories in weight space during training. Following, a population of such neural network models (refereed to as “model zoo”) would form topological structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can be reveal latent properties of individual models. With such zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of neural network weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of neural networks. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of neural network models for further research. In total the proposed model zoo dataset is based on six image datasets, consist of 27 model zoos with varying hyperparameter combinations are generated and includes 50’360 unique neural network models resulting in over 2’585’360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks as mentioned before. <strong>Dataset</strong> This dataset is part of a larger collection of model zoos and contains the zoo of 1000 ResNet18 models trained on Tiny Imagenet. All zoos with extensive information and code can be found at www.modelzoos.cc. The complete zoo is 2.6TB large. Due to the size, this repository contains the checkpoints of the first 115 models at their last epoch 60. For a link to the full dataset as well as more information on the zoos and code to access and use the zoos, please see www.modelzoos.cc.
DOI: 10.5281/zenodo.6974029
title: Model Zoo: A Dataset of Diverse Populations of Resnet-18 Models - CIFAR-10

description: <strong>Abstract</strong> In the last years, neural networks have evolved from laboratory environments to the state-of-the-art for many real-world problems. Our hypothesis is that neural network models (i.e., their weights and biases) evolve on unique, smooth trajectories in weight space during training. Following, a population of such neural network models (refereed to as “model zoo”) would form topological structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can be reveal latent properties of individual models. With such zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of neural network weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of neural networks. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of neural network models for further research. In total the proposed model zoo dataset is based on six image datasets, consist of 24 model zoos with varying hyperparameter combinations are generated and includes 47’360 unique neural network models resulting in over 2’415’360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks as mentioned before. <strong>Dataset</strong> This dataset is part of a larger collection of model zoos and contains the zoo of 1000 ResNet18 models trained on CIFAR10. All zoos with extensive information and code can be found at www.modelzoos.cc. The complete zoo is 2.3TB large. Due to the size, this repository contains the checkpoints of epochs 1, 10 and 50. For a link to the full dataset as well as more information on the zoos and code to access and use the zoos, please see www.modelzoos.cc.
DOI: 10.5281/zenodo.6977382
title: Model Zoo: A Dataset of Diverse Populations of Resnet-18 Models - CIFAR-100

description: <strong>Abstract</strong> In the last years, neural networks have evolved from laboratory environments to the state-of-the-art for many real-world problems. Our hypothesis is that neural network models (i.e., their weights and biases) evolve on unique, smooth trajectories in weight space during training. Following, a population of such neural network models (refereed to as “model zoo”) would form topological structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can be reveal latent properties of individual models. With such zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of neural network weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of neural networks. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of neural network models for further research. In total the proposed model zoo dataset is based on six image datasets, consist of 27 model zoos with varying hyperparameter combinations are generated and includes 50’360 unique neural network models resulting in over 2’585’360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks as mentioned before. <strong>Dataset</strong> This dataset is part of a larger collection of model zoos and contains the zoo of 1000 ResNet18 models trained on CIFAR100. All zoos with extensive information and code can be found at www.modelzoos.cc. The complete zoo is 2.6TB large. Due to the size, this repository contains the checkpoints of the last epoch 60. For a link to the full dataset as well as more information on the zoos and code to access and use the zoos, please see www.modelzoos.cc.
DOI: 10.5281/zenodo.6974028
title: Model Zoo: A Dataset of Diverse Populations of Resnet-18 Models - CIFAR-10

description: <strong>Abstract</strong> In the last years, neural networks have evolved from laboratory environments to the state-of-the-art for many real-world problems. Our hypothesis is that neural network models (i.e., their weights and biases) evolve on unique, smooth trajectories in weight space during training. Following, a population of such neural network models (refereed to as “model zoo”) would form topological structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can be reveal latent properties of individual models. With such zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of neural network weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of neural networks. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of neural network models for further research. In total the proposed model zoo dataset is based on six image datasets, consist of 24 model zoos with varying hyperparameter combinations are generated and includes 47’360 unique neural network models resulting in over 2’415’360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks as mentioned before. <strong>Dataset</strong> This dataset is part of a larger collection of model zoos and contains the zoo of 1000 ResNet18 models trained on CIFAR10. All zoos with extensive information and code can be found at www.modelzoos.cc. The complete zoo is 2.3TB large. Due to the size, this repository contains the checkpoints of epochs 1, 10 and 50. For a link to the full dataset as well as more information on the zoos and code to access and use the zoos, please see www.modelzoos.cc.
DOI: 10.5281/zenodo.6977381
title: Model Zoo: A Dataset of Diverse Populations of Resnet-18 Models - CIFAR-100

description: <strong>Abstract</strong> In the last years, neural networks have evolved from laboratory environments to the state-of-the-art for many real-world problems. Our hypothesis is that neural network models (i.e., their weights and biases) evolve on unique, smooth trajectories in weight space during training. Following, a population of such neural network models (refereed to as “model zoo”) would form topological structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can be reveal latent properties of individual models. With such zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of neural network weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of neural networks. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of neural network models for further research. In total the proposed model zoo dataset is based on six image datasets, consist of 27 model zoos with varying hyperparameter combinations are generated and includes 50’360 unique neural network models resulting in over 2’585’360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks as mentioned before. <strong>Dataset</strong> This dataset is part of a larger collection of model zoos and contains the zoo of 1000 ResNet18 models trained on CIFAR100. All zoos with extensive information and code can be found at www.modelzoos.cc. The complete zoo is 2.6TB large. Due to the size, this repository contains the checkpoints of the last epoch 60. For a link to the full dataset as well as more information on the zoos and code to access and use the zoos, please see www.modelzoos.cc.
DOI: 10.6084/m9.figshare.20652645
title: Raw qPCR results

description: Threshold cycle (Ct) values for amplification reactions carried out according to Supplementary Methods SM6, SM6a and SM6b from Golebiewski et al. 2022
DOI: 10.6084/m9.figshare.20652741.v1
title: Supplementary data for Roles of phytoplankton, microzooplankton, and bacteria in DMSP and DMS transformation processes in the northwestern Pacific continental sea

description: The distribution of relevant parameters in surface, sediment porewater, overlying seawater and sea surface microlayer.
DOI: 10.6084/m9.figshare.20652741
title: Supplementary data for Roles of phytoplankton, microzooplankton, and bacteria in DMSP and DMS transformation processes in the northwestern Pacific continental sea

description: The distribution of relevant parameters in surface, sediment porewater, overlying seawater and sea surface microlayer.
DOI: 10.6084/m9.figshare.20652915
title: Colony count data

description: Colony counts obtained according to methodoogy described in Golebiewski et al. 2022
DOI: 10.5281/zenodo.6451033
title: Data from: Grazer-induced aggregation in diatoms

description: Data from the paper "Grazer-induced aggregation in diatoms". The paper explores the grazer-induced change in stickiness of different diatoms species. Data includes measurements of stickiness of different diatoms when exposed to grazer cues in different experimental set-ups including duration, dose-response and nutrient-limited experiments.
DOI: 10.5281/zenodo.6451032
title: Data from: Grazer-induced aggregation in diatoms

description: Data from the paper "Grazer-induced aggregation in diatoms". The paper explores the grazer-induced change in stickiness of different diatoms species. Data includes measurements of stickiness of different diatoms when exposed to grazer cues in different experimental set-ups including duration, dose-response and nutrient-limited experiments.
DOI: 10.26106/2ngw-pd20
title: Supplementary material to the article "A negative career shock and career capital: the perspective of sports clubs personnel during the COIVD-19 pandemic" - concept
DOI: 10.23729/0f99ddd5-7aa1-4152-a085-6bdbc23a25a5
title: FERMAID Part 1 preview

description: First batch of samples for the FERMAID project, collective preview entry found at https://doi.org/10.23729/c9377f8c-9978-48d2-8dec-1f0daf2b0eb0 Preview of PAS dataset https://doi.org/10.23729/1d982b68-7b5f-430e-875e-8fa188d8bb92
DOI: 10.6084/m9.figshare.19690474
title: Comparing the renal outcomes in patients with atrial fibrillation receiving different oral anticoagulants

description: We aimed to compare renal function changes in patients with atrial fibrillation (AF) prescribed different oral anticoagulants (OACs). We performed a retrospective analysis of Australian national primary care data. A total of 12,562 patients with AF and initiated OAC between 1 January 2013 and 31 December 2017 were included. Inverse probability of treatment weighting was used for balancing baseline characteristics and the risks of decline in estimated glomerular filtration rate (eGFR) in patients prescribed each OAC were compared. Compared with warfarin, prescribing of direct-acting oral anticoagulants (DOACs) was associated with a lower risk of renal function decline per 1000 person-years: hazard ratio (HR) 0.75, 95% confidence interval (CI) 0.68–0.81, p &lt; 0.001 for ≥30% decline in eGFR; HR 0.28, 95% CI 0.20–0.41, p &lt; 0.001 for eGFR decline to ≤30 mL/min/1.73 m<sup>2</sup>; and HR 0.45, 95% CI 0.35–0.58, p &lt; 0.001 for serum creatinine doubling. Compared with dabigatran, rivaroxaban use had a significantly lowered risk of decline in eGFR to ≤30 mL/min/1.73 m<sup>2</sup> (HR 0.29, 95% CI 0.13–0.66, p = 0.003) and risk of doubling of serum creatinine (HR 0.62, 95% CI 0.40–0.95, p = 0.030). The risk of renal function decline appeared to be lower in patients prescribed DOACs versus warfarin.
DOI: 10.6084/m9.figshare.19690474.v1
title: Comparing the renal outcomes in patients with atrial fibrillation receiving different oral anticoagulants

description: We aimed to compare renal function changes in patients with atrial fibrillation (AF) prescribed different oral anticoagulants (OACs). We performed a retrospective analysis of Australian national primary care data. A total of 12,562 patients with AF and initiated OAC between 1 January 2013 and 31 December 2017 were included. Inverse probability of treatment weighting was used for balancing baseline characteristics and the risks of decline in estimated glomerular filtration rate (eGFR) in patients prescribed each OAC were compared. Compared with warfarin, prescribing of direct-acting oral anticoagulants (DOACs) was associated with a lower risk of renal function decline per 1000 person-years: hazard ratio (HR) 0.75, 95% confidence interval (CI) 0.68–0.81, p &lt; 0.001 for ≥30% decline in eGFR; HR 0.28, 95% CI 0.20–0.41, p &lt; 0.001 for eGFR decline to ≤30 mL/min/1.73 m<sup>2</sup>; and HR 0.45, 95% CI 0.35–0.58, p &lt; 0.001 for serum creatinine doubling. Compared with dabigatran, rivaroxaban use had a significantly lowered risk of decline in eGFR to ≤30 mL/min/1.73 m<sup>2</sup> (HR 0.29, 95% CI 0.13–0.66, p = 0.003) and risk of doubling of serum creatinine (HR 0.62, 95% CI 0.40–0.95, p = 0.030). The risk of renal function decline appeared to be lower in patients prescribed DOACs versus warfarin.
DOI: 10.6084/m9.figshare.20500647
title: REANG WOMEN AUTONOMY.sav

description: Dataset contains information on Reang tribe of India. Questions address the measures and determinants of reproductive agency.
DOI: 10.6084/m9.figshare.20500647.v1
title: REANG WOMEN AUTONOMY.sav

description: Dataset contains information on Reang tribe of India. Questions address the measures and determinants of reproductive agency.
DOI: 10.5281/zenodo.7023288
title: EmEL-V : EL++ Ontology Embeddings for Many-to-Many Relationships

description: Datasets used in experiments conducted as part of the paper -- EmEL-V : EL++ Ontology Embeddings for Many-to-Many Relationships
DOI: 10.5281/zenodo.7023287
title: EmEL-V : EL++ Ontology Embeddings for Many-to-Many Relationships

description: Datasets used in experiments conducted as part of the paper -- EmEL-V : EL++ Ontology Embeddings for Many-to-Many Relationships
DOI: 10.6084/m9.figshare.20653494
title: Supplementary table 1 from Immune function of the serosa in hemimetabolous insect eggs

description: Manual annotation of all immune genes found in the Oncopeltus fasciatus genome [41] using our de novo assembly. OFASnumbers refer to Oncopeltus fasciatus Official Gene Set 1.2 [28]. CDS and protein sequences are included
DOI: 10.6084/m9.figshare.20653494.v1
title: Supplementary table 1 from Immune function of the serosa in hemimetabolous insect eggs

description: Manual annotation of all immune genes found in the Oncopeltus fasciatus genome [41] using our de novo assembly. OFASnumbers refer to Oncopeltus fasciatus Official Gene Set 1.2 [28]. CDS and protein sequences are included
DOI: 10.6084/m9.figshare.20653497
title: Supplementary table 5 from Immune function of the serosa in hemimetabolous insect eggs

description: Significantly (adjusted p-value &lt; 0.05) upregulated (&gt;1.3 times) genes upon sterile injury (DEseq output). OFASnumbers refer to Oncopeltus fasciatus Official Gene Set 1.2 [28].
DOI: 10.6084/m9.figshare.20653497.v1
title: Supplementary table 5 from Immune function of the serosa in hemimetabolous insect eggs

description: Significantly (adjusted p-value &lt; 0.05) upregulated (&gt;1.3 times) genes upon sterile injury (DEseq output). OFASnumbers refer to Oncopeltus fasciatus Official Gene Set 1.2 [28].
DOI: 10.6084/m9.figshare.20653503
title: Supplementary table 3 from Immune function of the serosa in hemimetabolous insect eggs

description: Significantly (adjusted p-value &lt; 0.05) upregulated (&gt;1.3 times) genes upon septic injury (DEseq output). OFASnumbers refer to Oncopeltus fasciatus Official Gene Set 1.2 [28]