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opencc-by-4.0Jan 2022View details →

How does moving Public Engagement with Research Online Change Audience Diversity? Comparing Inclusion Indicators for 2019 & 2020 European Researchers' Night events

Taking place annually in more than 400 cities, European Researchers’ Night is a pan- European synchronized event that aims to bring researchers closer to the public. In this paper audience profiles are compared from events in 2019 and 2020. In 2019, face-to-face events reached an estimated 1.6 million attendees, while in 2020, events shifted online due to the COVID-19 pandemic and reached an estimated 2.3 million attendees. Focusing on social inclusion metrics, survey data is analyzed across two national contexts (Ireland and Malta) in 2019 (n=656) and 2020 (n=506). The results from this exploratory, descriptive study shed light on how moving public engagement with research online shifted audience profiles. Based on prior research about the digital divide in access and use of online media, hypotheses were proposed that online European Researchers’ Night events would attract audiences with higher educational attainment levels and greater self-reported, subjective economic well-being. While changes were observed from 2019 to 2020, results for each hypothesis show a mixed picture. The first hypothesis was upheld for the highest education levels but failed for the lowest levels suggesting that the pivot to online events simultaneously attracted participants with no formal education and those with postgraduate qualifications, while attracting less of those with undergraduate or lower levels of education. The second hypothesis was not upheld, with online European Researchers’ Night events attracting audiences with slightly higher levels of economic well-being compared to face-to-face events. The findings of this study indicate that European Researchers’ Night events present a clear opportunity to measure the effects of the digital divide in relation to public engagement with research across Europe.

opencc-by-4.0Jan 2022View details →

Raw data: Diversity in root architecture of durum wheat at stem elongation under drought stress

Raw data on above and below ground traits from a greenhouse drought stress experiment with six durum wheat varieties performed at Tuscia University, Viterbo, Italy. Measurements were performed at stem elongation stage; recorded traits: plant shoot length, dry weight, number of leaves and tillers; total root length, root surface area, mean diameter, volume, number of tips, forks, crossings, root dry weight and root angle. Root measurments were performed on the whole root system and the topsoil area (upper 5 cm).

opencc-by-4.0Feb 2022View details →

Data for article: A quantitative framework to infer the effect of traits, diversity and environment on dispersal and extinction rates from fossils

Supplementary information for: A quantitative framework to infer the effect of traits, diversity and environment on dispersal and extinction rates from fossils Torsten Hauffe, Mathias M. Pires, Tiago B. Quental, Thomas Wilke, and Daniele Silvestro   <ul> <li> Simulations <ul> <li>Scripts <ul> <li>Scenario1_SamplingHeterogeneity.R: Script to simulate biogeographic histories with sampling heterogeneity</li> <li>Scenario3_SealevelInvasion.R: Script to simulate biogeographic histories where sea level facilitates dispersal and invasion induces extinction</li> <li>Scenario3_DiversityDependence.R: Script to simulate diversity-dependent biogeographic histories</li> <li>Scenario4_TraitDependence.R: Script to simulate trait-dependent biogeographic histories</li> <li>Scenario5_CategoricalTraitDependence.R: Script to simulate trait-dependent biogeographic histories</li> </ul> </li> <li>Results <ul> <li>Scenario1_SamplingHeterogenetiy_alpha05.txt: Results of simulations scenario 1 with a sampling heterogeneity of alpha = 0.5</li> <li>Scenario1_SamplingHeterogenetiy_alpha1.txt: Results of simulations scenario 1 with a sampling heterogeneity of alpha = 1</li> <li>Scenario1_SamplingHeterogenetiy_alpha2.txt: Results of simulations scenario 1 with a sampling heterogeneity of alpha = 2</li> <li>Scenario1_SamplingHeterogenetiy_alpha10.txt: Results of simulations scenario 1 with a sampling heterogeneity of alpha = 10</li> <li>Scenario2_Independent_dispersal_and_extinction.txt: Results of simulation scenario 2 with sea-level independent dispersal and no invasion induced extinction</li> <li>Scenario2_Sealevel_dependent_dispersal_and_independent_extinction.txt: Results of simulation scenario 2 with sea-level dependent dispersal and no invasion induced extinction</li> <li>Scenario2_Sealevel_independent_dispersal_and_invasion_induced_extinction.txt: Results of simulation scenario 2 with sea-level independent dispersal and invasion induced extinction</li> <li>Scenario2_Sealevel_dependent_dispersal_and_invasion_induced_extinction.txt: Results of simulation scenario 2 with sea-level dependent dispersal and invasion induced extinction</li> <li>Scenario3_Independent_dispersal_and_extinction.txt: Results of simulations scenario 3 with diversity-independent dispersal and extinction</li> <li>Scenario3_Diversity_dependent_dispersal_and_independent_extinction.txt: Results of simulations scenario 3 with diversity-dependent dispersal and diversity-independent extinction</li> <li>Scenario3_Independent_dispersal_and_Diversity_dependent_extinction.txt: Results of simulations scenario 3 with diversity-dependent dispersal and diversity-independent extinction</li> <li>Scenario3_Diversity_dependent_dispersal_and_extinction.txt: Results of simulations scenario 3 with diversity-dependent dispersal and extinction</li> <li>Scenario4_Independent_dispersal_and_extinction.txt: Results of scenario 4 with trait-independent dispersal and extinction</li> <li>Scenario4_Trait_dependent_dispersal_and_independent_extinction.txt: Results of scenario 4 with trait-dependent dispersal and independent extinction</li> <li>Scenario4_Independent_dispersal_and_trait_dependent_extinction.txt: Results of scenario 4 with independent dispersal and trait-dependent extinction</li> <li>Scenario4_trait_dependent_dispersal_and_extinction.txt: Results of scenario 4 with trait-dependent dispersal and extinction</li> <li>Scenario5_CatTrait_dependent_dispersal_and_independent_extinction.txt: Results of model 2 with categorical traits (e.g family) influence dispersal but no influence of a category-specific continuous traits</li> </ul> </li> </ul> </li> <li>Carnivora <ul> <li>BinnedOccurrence: Folder with 100 replicates of binned occurrences of max. 330 carnivoran genera throughout the Neogene</li> <li>BodyMass: Folder with 100 replicates of body mass for 330 carnivoran genera</li> <li>Sealevel: Folder with sea level through the Neogene</li> <li>Temperature: Folder with the temperature record of the Neogene</li> <li>Families: Folder with families as taxonomic proxy for phylogeny. FamilyGeneraNumeric.txt is the numeric coding used for the Bayesian analyses of carnivoran biogeography</li> </ul> </li> </ul>

opencc-by-4.0Feb 2022View details →

Supplementary material for "High semi-natural vegetation cover and heterogeneity of field sizes promote bird beta-diversity at larger scales in Ethiopian Highlands"

Abstract <ol> <li>The intensification of farming practices exerts detrimental effects on biodiversity. Most research has focused on declines in species richness at local scales (alpha-diversity) although species loss is exacerbated by biotic homogenization that operates at larger scales (i.e., affecting beta-diversity). The majority of studies have been conducted in temperate, industrialized countries while tropical areas remain poorly studied. Agricultural landscapes of sub-Saharan Africa are still largely dominated by small-scale subsistence farming, but strenuous efforts to intensify farming practices are currently spreading to meet a growing food demand. It is therefore crucial to understand how these intensified practices affect biodiversity to mitigate their negative impacts. </li> <li>We investigated how farming system (small- vs large-scale farming) and landscape complexity (semi-natural vegetation cover) drive bird species composition, community turnover, and beta-diversity patterns in Ethiopian Highlands’ agroecosystems. We evaluated the following hypotheses: (1) large-scale farming homogenizes bird communities, (2) community turnover is higher in small-scale farms, (3) interactive effects between landscape complexity and farming systems shape avian communities, (4) heterogeneity of field sizes increases community turnover at larger scales. </li> <li>Bird communities underwent greater compositional changes along the landscape complexity than along the agricultural intensity gradient. Contrary to our expectations, beta-diversity was not significantly lower within large-scale farms (no biotic homogenization), and complex landscapes that still offer a high amount of semi-natural vegetation promoted community turnover in both farming systems. </li> <li>Semi-natural vegetation cover mediated how avian communities responded to agricultural intensification: the compositional differences between small- and large-scale farms increased with vegetation cover, further promoting avian community heterogeneity at the landscape level.</li> <li>The heterogeneity in field sizes also enhanced bird community turnover, suggesting that a combination of both small- and large-scale farming systems within a given landscape unit would promote beta-diversity at larger scales, provided large-scale farms do not become dominant.</li> <li>Synthesis and applications:  Landscape complexity shaped avian communities to a stronger degree than farming intensity, emphasizing the importance of semi-natural vegetation and landscape heterogeneity for the maintenance of diverse bird communities and for achieving multifunctional landscapes promoting biodiversity and associated ecosystem services on the High Ethiopian plateaus.   </li> </ol>

opencc-by-4.0Apr 2022View details →

Diversity in the Expressed Genomic Host Response to Myocardial Infarction - Validation Dataset

External validation was performed by separately hierarchically clustering 934 patients with STEMI in an independent cohort[1] into 2 groups (232 and 702 individuals) based on Illumina HT12v4-profiled PBMC expression (median time 21 hour between cardiac catheterization and blood sampling). Probes with most variable expression intensities (SD≥0.5, 216 probes, excluding ribosomal genes) were used. From the 20 most differentially expressed genes in the discovery cohort described in the manuscript Toma et al. 2022 [2], 19 were available in the validation cohort. Column names include the Illumina identifyer and the mapped gene name as used in the discovery cohort. Values are log2-transformed, quantile-normalized, batch-corrected values, see also [1] for methodological details. Acknowledgement: This work is supported by LIFE – Leipzig Research Center for Civilization Diseases, Universität Leipzig. LIFE is funded by means of the European Union, by the European Regional Development Fund (ERDF) and by means of the Free State of Saxony within the framework of the excellence initiative.   1) Teren A, Kirsten H, Beutner F, Scholz M, Holdt LM, Teupser D, Gutberlet M, Thiery J, Schuler G, Eitel I. Alteration of multiple leukocyte gene expression networks is linked with magnetic resonance markers of prognosis after acute st-elevation myocardial infarction. Scientific Reports. 2017;7:41705 2) Toma A, dos Santos C, Burzyńska B, Góra M, Kiliszek M, Stickle N, Kirsten H, Kosyakovsky L, Wang B, van Diepen S, Epelman S, Szekely Y, Marshall JC, Billia F, Lawler PR (2022), Diversity in the Expressed Genomic Host Response to Myocardial Infarction, submitted.

opencc-by-4.0May 2022View details →

1-km forest tree height, cover, plant area index, and foliage height diversity for the CONUS

Consistent and spatially explicit periodic monitoring of forest structure is essential for estimating forest-related carbon emissions, analyzing forest degradation, and supporting sustainable forest management policies.  To date, few products are available that allow for continental to global operational monitoring of changes in canopy structure.  In this study, we explored the synergy between the NASA’s spaceborne Global Ecosystem Dynamics Investigation (GEDI) waveform LiDAR and the Visible Infrared Imaging Radiometer Suite (VIIRS) data to produce spatially explicit and consistent annual maps of canopy height (CH), percent canopy cover (PCC), plant area index (PAI), and foliage height diversity (FHD) across the conterminous United States (CONUS) at 1-km resolution for 2013-2020.  The accuracies of the annual maps were assessed using forest structure attribute derived from airborne laser scanning (ALS) data acquired between 2013 and 2020 for the 48 National Ecological Observatory Network (NEON) field sites distributed across the CONUS.  The root mean square error (RMSE) values of the annual canopy height maps as compared with the ALS reference data varied from a minimum of 3.31-m for 2020 to a maximum of 4.19-m for 2017.  Similarly, the RMSE values for PCC ranged between 8% (2020) and 11% (all other years).  Qualitative evaluations of the annual maps using time series of very high-resolution images further suggested that the VIIRS-derived products could capture both large and “more” subtle changes in forest structure associated with partial harvesting, wind damage, wildfires, and other environmental stresses.

opencc-by-4.0May 2022View details →

Diversity of options to eliminate fossil fuels and reach carbon-neutrality across the entire European energy system

Sector-coupled Euro-Calliope model outputs The subdirectories found here cover cost-optimal and cost relaxation (SPORES) carbon-neutrality runs for a sector-coupled, sub-national resolution European energy system model. The underlying model to produce these results, <a href="https://github.com/calliope-project/sector-coupled-euro-calliope">Sector-coupled Euro-Calliope</a>, is an extension of the power-sector only <a href="https://github.com/calliope-project/euro-calliope">Euro-Calliope model</a>. It incorporates all energy consuming sectors and includes a more detailed representation of transmission capacities between 98 model regions in Europe. The model runs here are based on specific Sector-Coupled Euro-Calliope minor releases: <ul> <li><a href="https://github.com/calliope-project/euro-calliope-2.0/commit/74f6a9b2e157b6147e155b556f521c03ef23246a">cost-opt</a></li> <li><a href="https://github.com/calliope-project/euro-calliope-2.0/commit/519a4fb26920114e451b8247b38ed86b93b6af89">slack-*</a></li> </ul> The models were optimised using the <a href="https://github.com/calliope-project/calliope">Calliope open energy system modelling framework</a>, again based on different minor releases: <ul> <li><a href="https://github.com/calliope-project/calliope/commit/1faed85eeddbe41c29d52982a6bfb147ef9001a3">cost-opt</a></li> <li><a href="https://github.com/calliope-project/calliope/commit/19460da2e23e752995a9a02ae6dca49379565d43">slack-*</a></li> </ul> <code>slack-*</code> results are for cost relaxation runs, where <code>*</code> refers to the percentage relaxation from the optimal cost of the 2018 energy system. All results use the <a href="https://github.com/sentinel-energy/friendly_data">friendly data</a> format. Data files are structured according to standardised sector-coupled Euro-Calliope output processing provided by the <a href="https://github.com/brynpickering/friendly-calliope">friendly-calliope</a> package + additional processing to produce data relevant to nine high-level metrics (see script <a href="https://github.com/calliope-project/sector-coupled-euro-calliope/blob/main/src/analyse/result_to_friendly.py">here</a>). Both cost optimal and SPORES results related to a projected demand scenario are given in the directories ending in "demand-update". To explore the data, please refer to the <a href="https://sentinel-energy.github.io/friendly_data/">friendly data documentation</a>.

opencc-by-4.0May 2022View details →

"Centenarians have a diverse population of gut bacteriophages that may promote healthy lifespan" - Genomes and annotation

File-dump associated with the manuscript: "Centenarians have a diverse population of gut bacteriophages that may promote healthy lifespan" (Not yet published) MGVs refer to the viral genome database in the publication: https://www.nature.com/articles/s41564-021-00928-6    Following uploaded: File 1: VOG Markers in vOTUs/vMAGs and MGV genomes File 2: Viral Tree Newick file with vOTUs/vMAGs and MGV genomes File 3: All vOTUs/vMAGs genomes File 4: Master table annotation of vOTUs/vMAGs File 5: Centenarian bacterial isolate proviruses

opencc-by-4.0Mar 2022View details →

Systematic assessment of pathway databases, based on a diverse collection of user-submitted experiments

Supplemental data for the manuscript  "Systematic assessment of pathway databases, based on a diverse collection of user-submitted experiments". Content <ul> <li>functional_annotations.tar.gz  <ul> <li> functional annotations for 10 different functional annotation systems, for 5090 species</li> </ul> </li> <li> genome_info_and_statistics.tar.gz  <ul> <li>basic genome info, annotation system statistics, user query statistics</li> </ul> </li> <li>example_user_queries.tar.gz <ul> <li>three example files for user query inputs used in the analysis </li> </ul> </li> <li>README.txt <ul> <li>details about the files and file formats</li> </ul> </li> </ul>

Model Zoo: A Dataset of Diverse Populations of Neural Network Models - CIFAR10

Abstract 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. Dataset This dataset is part of a larger collection of model zoos and contains the zoos trained on CIFAR10. All zoos with extensive information and code can be found at www.modelzoos.cc. This repository contains two types of files: the raw model zoos as collections of models (file names beginning with "cifar_"), as well as preprocessed model zoos wrapped in a custom pytorch dataset class (filenames beginning with "dataset"). Zoos are trained with small and large CNN models, in three configurations varying the seed only (seed), varying hyperparameters with fixed seeds (hyp_fix) or varying hyperparameters with random seeds (hyp_rand). The index_dict.json files contain information on how to read the vectorized models. For more information on the zoos and code to access and use the zoos, please see www.modelzoos.cc.

Model Zoo: A Dataset of Diverse Populations of Neural Network Models - STL10 - Raw Datasets

Abstract 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. Dataset This dataset is part of a larger collection of model zoos and contains the zoos trained on the labelled samples from STL10. All zoos with extensive information and code can be found at www.modelzoos.cc. This repository contains the raw model zoos as collections of models (file names beginning with "cifar_"). Zoos are trained with small and large CNN models, in three configurations varying the seed only (seed), varying hyperparameters with fixed seeds (hyp_fix) or varying hyperparameters with random seeds (hyp_rand). Due to the large filesize, the preprocessed datasets are hosted in a separate repository. The index_dict.json files contain information on how to read the vectorized models. For more information on the zoos and code to access and use the zoos, please see www.modelzoos.cc.

Model Zoo: A Dataset of Diverse Populations of Neural Network Models - SVHN

Abstract 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. Dataset This dataset is part of a larger collection of model zoos and contains the zoos trained on the labelled samples from SVHN. All zoos with extensive information and code can be found at www.modelzoos.cc. This repository contains two types of files: the raw model zoos as collections of models (file names beginning with "svhn_"), as well as preprocessed model zoos wrapped in a custom pytorch dataset class (filenames beginning with "dataset"). Zoos are trained in three configurations varying the seed only (seed), varying hyperparameters with fixed seeds (hyp_fix) or varying hyperparameters with random seeds (hyp_rand). The index_dict.json files contain information on how to read the vectorized models. For more information on the zoos and code to access and use the zoos, please see www.modelzoos.cc.

Model Zoo: A Dataset of Diverse Populations of Neural Network Models - Fashion-MNIST

Abstract 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. Dataset This dataset is part of a larger collection of model zoos and contains the zoos trained on the labelled samples from Fashion-MNIST. All zoos with extensive information and code can be found at www.modelzoos.cc. This repository contains two types of files: the raw model zoos as collections of models (file names beginning with "fmnist_"), as well as preprocessed model zoos wrapped in a custom pytorch dataset class (filenames beginning with "dataset"). Zoos are trained in three configurations varying the seed only (seed), varying hyperparameters with fixed seeds (hyp_fix) or varying hyperparameters with random seeds (hyp_rand). The index_dict.json files contain information on how to read the vectorized models. For more information on the zoos and code to access and use the zoos, please see www.modelzoos.cc.

Model Zoo: A Dataset of Diverse Populations of Neural Network Models - MNIST

Abstract 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. Dataset This dataset is part of a larger collection of model zoos and contains the zoos trained on the labelled samples from MNIST. All zoos with extensive information and code can be found at www.modelzoos.cc. This repository contains two types of files: the raw model zoos as collections of models (file names beginning with "mnist_"), as well as preprocessed model zoos wrapped in a custom pytorch dataset class (filenames beginning with "dataset"). Zoos are trained in three configurations varying the seed only (seed), varying hyperparameters with fixed seeds (hyp_fix) or varying hyperparameters with random seeds (hyp_rand). The index_dict.json files contain information on how to read the vectorized models. For more information on the zoos and code to access and use the zoos, please see www.modelzoos.cc.

Data for: Seasonal dynamics of faunal diversity and population ecology in an estuarine seagrass bed

These are the data used in the analyses described in the paper titled "Seasonal dynamics of faunal diversity and population ecology in an estuarine seagrass bed", accepted at Estuaries and Coasts. We acknowledge the tangata whenua for the rohe in which these data were collected, Ngāi Tārewa and Ngāti Īrakehu. We thank the Akaroa Taiāpure for their support of this research. The data included are: Raw count data of taxa for each tow, associated with additional metadata including the date of collection, tow coordinates, and estimated seagrass cover (MonthlyRawSampling_Duvauchelle_2020.csv). This data was put through cleaning steps outlined in the file docs/dataCleaning.Rmd prior to being used in any analyses. The cleaned community composition data (cleanedCommunity.csv), output from <a href="https://github.com/spflanagan/ecology-duvauchelle/blob/main/docs/dataCleaning.Rmd">docs/dataCleaning.Rmd</a> and used in the downstream community and population analyses. The GPS coordinates for the tows (gpsdat.csv). These were extracted from the raw data in the data cleaning process. NZsyngnathids_measurements.csv contains the measurements of the pipefish from images. These data also underwent a cleaning process documented in <a href="https://github.com/spflanagan/ecology-duvauchelle/blob/main/docs/dataCleaning.Rmd">docs/dataCleaning.Rmd</a>. The cleaned pipefish trait data (pipefishTraits.csv), output from docs/dataCleaning.Rmd and used in the downstream population analysis documented in <a href="https://github.com/spflanagan/ecology-duvauchelle/blob/main/docs/populationAnalyses.Rmd">docs/populationAnalyses.Rmd</a>.

Microsatellite genotypes for «Genetic diversity and spatial genetic structure support the specialist‑generalist variation hypothesis in two sympatric woodpecker species»

Species are often arranged along a continuum from “specialists” to “generalists”. Specialists typically use fewer resources, occur in more patchily distributed habitats and have overall smaller population sizes than generalists. Accordingly, the specialist-generalist variation hypothesis (SGVH) proposes that populations of habitat specialists have lower genetic diversity and are genetically more differentiated due to reduced gene flow compared to populations of generalists. Here, expectations of the SGVH were tested by examining genetic diversity, spatial genetic structure and contemporary gene flow in two sympatric woodpecker species differing in habitat specialization. Compared to the generalist great spotted woodpecker (Dendrocopos major), lower genetic diversity was found in the specialist middle spotted woodpecker (Dendrocoptes medius). Evidence for recent bottlenecks was revealed in some populations of the middle spotted woodpecker, but in none of the great spotted woodpecker. Substantial spatial genetic structure and a significant correlation between genetic and geographic distances were found in the middle spotted woodpecker, but only weak spatial genetic structure and no significant correlation between genetic and geographic distances in the great spotted woodpecker. Finally, estimated levels of contemporary gene flow did not differ between the two species. Results are consistent with all but one expectations of the SGVH. This study adds to the relatively few investigations addressing the SGVH in terrestrial vertebrates.

Data and script: Reduced enumeration effort, but not coarse taxonomic resolution, is sufficient to represent beta diversity patterns of stream benthic diatoms

This is a dataset on benthic diatom communities sampled in 90 riffles (the local communities) within nine near-pristine subtropical streams (each stream represented a metacommunity) in southeast subtropical Brazil.  In addition to the dataset, we also provide the R code used to investigate whether reduced enumeration efforts (i.e., subsets of counted valves per sample) and the identification to the genus level are sufficient to recover patterns in the species composition and in beta diversity of benthic diatom metacommunities.

Raw sequencing data PhD Mixoplankton spatio-temporal diversity and its environmental drivers in the North Sea

Raw sequencing data PhD Mixoplankton spatio-temporal diversity and its environmental drivers in the North Sea

opencc-by-4.0Feb 2022View details →

SubductCR17_MAG-redox_and_CFP_diversity

The code and data connected to the "Biology Meets Subduction" Metagenomic analysis of the 2017 Costa Rica project.