Find research datasets worth reusing

Data from: Seasonal dynamics of flock interaction networks across a human-modified landscape in lowland Amazonian rainforest

Although lowland tropical rainforests were once widely believed to be the archetype of stability, seasonal variation exists. In these environments, seasonality is defined by rainfall, leading to a predictable pattern of biotic and abiotic changes. Only the full annual cycle reveals niche breadth, yet most studies of tropical organisms ignore seasonality, thereby underestimating realized conditions. If human-modified habitats display more seasonal stress than intact habitats, then ignoring seasonality will have particularly important repercussions for conservation. We examined the seasonal dynamics of Amazonian mixed-species flocks—an important species interaction network—across three habitats with increasing human disturbance. We quantified seasonal space use, species richness and attendance, and four ecological network metrics for flocks in primary forest, small forest fragments, and regenerating secondary forest in central Amazonia. Our results indicate that, even in intact, lowland rainforest, mixed-species flocks exhibit seasonal differences. During the dry season, flocks included more species, generally ranged over larger areas, and displayed network structures that were less complex and less cohesive. We speculate that because most flocking species nest during the dry season—a time of reduced arthropod abundance—flocks are simultaneously constrained by these two competing pressures. Moreover, these seasonal differences were most pronounced in forest fragments and secondary forest, habitats that are less buffered from the changing seasons. Our results suggest that seasonality influences the conservation value of human-modified habitats, raising important questions about how rainforest organisms will cope with an increasingly unstable climate.

Dataset - A complex network of additive and epistatic quantitative trait loci underlies natural variation of Arabidopsis thaliana quantitative disease resistance to Ralstonia solanacearum under heat stress

Plant immunity is often negatively impacted by heat stress. However, the underlying molecular mechanisms remain poorly characterized. Based on a genome-wide association mapping approach, this study aims to identify in Arabidopsis thaliana the genetic bases of robust resistance mechanisms to the devastating pathogen Ralstonia solanacearum under heat stress. A local mapping population was phenotyped against the R. solanacearum GMI1000 strain at 27 and 30 °C. To obtain a precise description of the genetic architecture underlying natural variation of quantitative disease resistance (QDR), we applied a genome-wide local score analysis. Alongside an extensive genetic variation found in this local population at both temperatures, we observed a playful dynamics of quantitative trait loci along the infection stages. In addition, a complex genetic network of interacting loci could be detected at 30 °C. As a first step to investigate the underlying molecular mechanisms, the atypical meiotic cyclin SOLO DANCERS gene was validated by a reverse genetic approach as involved in QDR to R. solanacearum at 30 °C. In the context of climate change, the complex genetic architecture underlying QDR under heat stress in a local mapping population revealed candidate genes with diverse molecular functions.

Data from: Incorporating alternative interaction modes, forbidden links and trait-based mechanisms increases the minimum trait dimensionality of ecological networks

<ol> <li>Individual-level traits mediate interaction outcomes and community structure. It is important, therefore, to identify the minimum number of traits that characterise ecological networks, i.e. their 'minimum dimensionality'. Existing methods for estimating minimum dimensionality often lack three features associated with increased trait numbers: alternative interaction modes (e.g. feeding strategies such as active vs. sit-and-wait feeding), trait-mediated 'forbidden links' and a mechanistic description of interactions. Omitting these features can underestimate the trait numbers involved, and therefore, minimum dimensionality. We develop a 'minimum mechanistic dimensionality' measure, accounting for these three features.</li> <li>The only input our method requires is the network of interaction outcomes. We assume how traits are mechanistically involved in alternative interaction modes. These unidentified traits are contrasted using pairwise performance inequalities between interacting species. For example, if a predator feeds upon a prey species via a typical predation mode, in each step of the predation sequence the predator's performance must be greater than the prey's. We construct a system of inequalities from all observed outcomes, which we attempt to solve with mixed integer linear programming. The number of traits required for a feasible system of inequalities provides our minimum dimensionality estimate.</li> <li>We applied our method to 658 published empirical ecological networks including primary consumption, predator–prey, parasitism, pollination, seed dispersal and animal dominance networks, to compare with minimum dimensionality estimates when the three focal features are missing. Minimum dimensionality was typically higher when including alternative interaction modes (54% of empirical networks), 'forbidden interactions' as trait-mediated interaction outcomes (92%), or a mechanistic perspective (81%), compared to estimates missing these features. Additionally, we tested minimum dimensionality estimates on simulated networks with known dimensionality. Our method typically estimated a higher minimum dimensionality, closer to the actual dimensionality, while avoiding the overestimation associated with a previous method.</li> <li>Our method can reduce the risk of omitting traits involved in different interaction modes, in failure outcomes, or mechanistically. More accurate estimates will allow us to parameterise models of theoretical networks with more realistic structure at the interaction outcome level. Thus, we hope our method can improve predictions of community structure and structure-dependent dynamics.</li> </ol>

opencc-by-4.0Aug 2020View details →

Citation network nodelist, edgelist, and visualization (UNIGE MA Thesis)

The data in this deposit were used in the <a href="https://archive-ouverte.unige.ch/unige:140928">author's Thesis</a> for the degree of Master of Arts in Philosophy with Specialization in the Philosophy of Science, under the supervision of Prof. Marcel Weber (Department of Philosophy, University of Geneva, Switzerland). This deposit contains three files: (i) a list of nodes; (ii) a list of edges; and (iii) a digital image of the network as it appears in the Appendix A of the Thesis. The lists of nodes and edges contained here were manually built by the author, and the visualization was obtained using the software Gephi. The nodes represent specific texts related to the historical development of Expected Utility Theory (as explained in section 3.1 of the Thesis), corresponding to the texts registered in the Citation Network Texts section of the References of the Thesis. The data and visualization in this deposit use the convention "authorsORIGINALYEAR". For example, Pareto (1909/1979) is represented as "pareto1909", and Safra et al. (1990a) as "safra.etal1990a". The color of each node depends on the subsection of the historical description (section 3.2) they appear in, corresponding to the categories of the "topic" column of the nodelist. And their sizes are proportional to how many times they were mentioned by others in the network. The edges represent citations between texts and their colors represent mention types (as defined in section 3.1), such that agreement are in green, disagreements are in light blue, and neutral mentions are in light gray. The reasons for the final categorization of potentially unclear mention types are noted in the "reasons" column.

Data from: Modelling the current and future biodiversity distribution in the Chilean Mediterranean Hotspot. The role of protected areas network in a warmer future

Aim: Mediterranean Chile is part of the five recognized Mediterranean-type climates in the world and harbors a very rich floral diversity. Climate change has been reported as a significant threat to its biodiversity. We used the flora of Mediterranean Chile to analyze how biodiversity patterns, as measured by Phylogenetic Diversity, genus and species richness will respond to climate change scenarios and identify the areas that will harbor the greatest evolutionary potential and biodiversity richness. We also evaluated how these spatial patterns are depicted within the current network of protected areas. Location: Chilean Mediterranean climate-type Region, South America. Methods: Biodiversity metrics were evaluated for current and future climatic scenarios. Species distribution models were done using Maxent for 1.727 species and 571 genera. Relationships between species/genera gain, loss and turnover were evaluated. For Mediterranean endemic species, loss and gain was also related to life form. Finally, variation in species gain, loss and turnover was evaluated in future climate change scenarios within and outside Mediterranean Chile state protected areas. Results: We found a general decrease in species richness in the entire Region toward future climate change scenarios. Phylogenetic Diversity is predicted to be higher than expected by richness in the north and south of the area, and lower than expected by richness in the Andes mountain. The highest average species and genus loss is predicted to occur outside the protected areas, meanwhile species and genus gain is higher within them. Main conclusions: Future biodiversity patterns are reported here for the first time in the Chilean Mediterranean Region. Our findings enhance the importance of the current protected areas to harbor this future variation, despite their reduced number and size along the region.

Integrating functional connectivity in designing networks of protected areas under climate change: a caribou case-study

Land-use change and climate change are recognized as two main drivers of the current biodiversity decline. Protected areas help safeguard the landscape from additional anthropogenic disturbances and, when properly designed, can help species cope with climate change impacts. When designed to protect the regional biodiversity rather than to conserve focal species or landscape elements, protected areas need to cover a representative sample of the regional biodiversity and be functionally connected, facilitating individual movements among protected areas in a network to maximize their effectiveness. We developed a methodology to define effective protected areas to implement in a regional network using ecological representativeness and functional connectivity as criteria. We illustrated this methodology in the Gaspésie region of Québec, Canada. We simulated movements for the endangered Atlantic-Gaspésie caribou population (Rangifer tarandus caribou), using an individual-based model, to determine functional connectivity based on this large mammal. We created multiple protected areas network scenarios and evaluated their ecological representativeness and functional connectivity for the current and future conditions. We selected a subset of the most effective network scenarios and extracted the protected areas included in them. There was a tradeoff between ecological representativeness and functional connectivity for the created networks. Only a few protected areas among those available were repeatedly chosen in the most effective networks. Protected areas maximizing both ecological representativeness and functional connectivity represented suitable areas to implement in an effective protected areas network. These areas ensured that a representative sample of the regional biodiversity was covered by the network, as well as maximizing the movement over time between and inside the protected areas for the focal population.

Recipient and donor characteristics govern the hierarchical structure of heterospecific pollen competition networks

<ul> <li>Pollinator sharing can have negative consequences for plant fitness with the arrival of foreign (i.e. heterospecific) pollen, yet responses are often variable among species. Plant traits and relatedness of donor and recipient species have been suggested to drive the variations in plant fitness, but how they shape the structure of pollen competition networks has been overlooked at the community level.</li> <li>To understand the importance of reproductive traits and relatedness on the impacts of heterospecific pollen we conducted a controlled glasshouse experiment with an artificial co-flowering community. We performed 1800 reciprocal crosses by experimentally transferring 50% and 100% foreign pollen among 10 species belonging to three different plant families.</li> <li>We found a significant reduction in seed set with 50% foreign pollen for 67% of the crosses driven largely by recipient traits and the interaction between recipient-donor traits under specific circumstances of trait-matching. In general, species with shorter styles, smaller stigmas and lower pollen:ovule ratios were more impacted by foreign pollen. These traits and their differences among species led to a hierarchical (or transitive) structure of pollen competition with clear winners and losers. However, phylogenetic distance among recipient and donor species did not explain the effects.</li> <li> Synthesis: Our study shows that specific traits and trait combinations between donor and recipient species are important in determining fitness outcomes with heterospecific pollen deposition. Moreover, the differences in traits between species lead to a competitive structure with clear "winners" or "losers" species. The results of this study indicate the need to shift from pairwise to community level interactions to elucidate the mechanisms underlying foreign pollen impacts upon plant reproductive fitness.</li> </ul>

Data from: Accelerating homogenization of the global plant–frugivore meta-network

Introductions of species by humans are causing the homogenization of species composition across biogeographic barriers. The ecological and evolutionary consequences of introduced species derive from their effects on networks of species interactions, but we lack a quantitative understanding of the impacts of introduced species on ecological networks and their biogeographic patterns globally. Here we address this data gap by analysing mutualistic seed-dispersal interactions from 410 local networks, encompassing 24,455 unique pairwise interactions between 1,631 animal and 3,208 plant species. We show that species introductions reduce biogeographic compartmentalization of the global meta-network, in which nodes are species and links are interactions observed within any local network. This homogenizing effect extends across spatial scales, decreasing beta diversity among local networks and modularity within networks. The prevalence of introduced interactions is directly related to human environmental modification and is accelerating, having increased sevenfold over the past 75 years. These dynamics alter the coevolutionary environments that mutualists experience, and we find that introduced species disproportionately interact with other introduced species. These processes are likely to amplify biotic homogenization in future ecosystems and may reduce the resilience of ecosystems by allowing perturbations to propagate more quickly and exposing disparate ecosystems to similar drivers. Our results highlight the importance of managing the increasing homogenization of ecological complexity.

Adjacency matrices and nodal attributes for prestige and homophily predict network structure for social learning of medicinal plant knowledge

Human subsistence societies have thrived in environmental extremes while maintaining biodiversity through social learning of ecological knowledge, such as techniques to prepare food and medicine from local resources. However, there is limited understanding of which processes shape social learning patterns and configuration in ecological knowledge networks, or how these processes apply to resource management and biological conservation. In this study, we test the hypothesis that the prestige (rarity or exclusivity) of knowledge shapes social learning networks. In addition, we test whether people tend to select who to learn from based on prestige (knowledge or reputation), and homophily (e.g., people of the same age or gender). We used interviews to assess five types of medicinal plant knowledge and how 303 people share this knowledge across four villages in Solomon Islands. We developed exponential random graph models (ERGMs) to test whether hypothesized patterns of knowledge sharing based on prestige and homophily are more common in the observed network than in randomly simulated networks of the same size. We found that prestige predicts five hypothesized network configurations and all three hypothesized learning patterns, while homophily predicts one of three hypothesized network configurations and five of the seven hypothesized learning patterns. These results compare the strength of different prestige and homophily effects on social learning and show how cultural practices such as intermarriage can affect certain aspects of prestige and homophily. By advancing our understanding of how prestige and homophily affect ecological knowledge networks, we identify which social learning patterns have the largest effects on biocultural conservation of ecological knowledge.

A human IgSF cell-surface interactome reveals a complex network of protein-protein interactions

Cell-surface protein-protein interactions (PPIs) mediate cell-cell communication, recognition and responses. We executed an interactome screen of 564 human cell-surface and secreted proteins, most of which are immunoglobulin superfamily (IgSF) proteins, using a high-throughput, automated ELISA-based screening platform employing a pooled-protein strategy to test all 318,096 PPI combinations. Screen results, augmented by phylogenetic homology analysis, revealed ~380 previously unreported PPIs. We validated a subset using surface plasmon resonance and cell binding assays. Observed PPIs reveal a large and complex network of interactions both within and across biological systems. We identified new PPIs for receptors with well-characterized ligands, and binding partners for 'orphan' receptors. New PPIs include proteins expressed on multiple cell types, and involved in diverse processes including immune and nervous system development and function, differentiation/proliferation, metabolism, vascularization, and reproduction. These PPIs provide a resource for further biological investigation into their functional relevance, and may offer new therapeutic drug targets.

opencc-by-4.0Feb 2020View details →

SONYC Urban Sound Tagging (SONYC-UST): a multilabel dataset from an urban acoustic sensor network

SONYC Urban Sound Tagging (SONYC-UST): a multilabel dataset from an urban acoustic sensor network Version 2.3, September 2020   Created by Mark Cartwright (1,2,3), Jason Cramer (1), Ana Elisa Mendez Mendez (1), Yu Wang (1), Ho-Hsiang Wu (1), Vincent Lostanlen (1,2,4), Magdalena Fuentes (1), Graham Dove (2), Charlie Mydlarz (1,2), Justin Salamon (5), Oded Nov (6), Juan Pablo Bello (1,2,3) <ol> <li>Music and Audio Research Lab, New York University</li> <li>Center for Urban Science and Progress, New York University</li> <li>Department of Computer Science and Engineering, New York University</li> <li>Cornell Lab of Ornithology</li> <li>Adobe Research</li> <li>Department of Technology Management and Innovation, New York University</li> </ol>   Publication If using this data in an academic work, please reference the DOI and version, as well as cite the following paper, which presented the data collection procedure and the first version of the dataset: Cartwright, M., Cramer, J., Mendez, A.E.M., Wang, Y., Wu, H., Lostanlen, V., Fuentes, M., Dove, G., Mydlarz, C., Salamon, J., Nov, O., Bello, J.P. SONYC-UST-V2: An Urban Sound Tagging Dataset with Spatiotemporal Context. In Proceedings of the Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), 2020. <a href="https://arxiv.org/abs/2009.05188">[pdf]</a>   Description SONYC Urban Sound Tagging (SONYC-UST) is a dataset for the development and evaluation of machine listening systems for realistic urban noise monitoring. The audio was recorded from the <a href="https://wp.nyu.edu/sonyc">SONYC</a> acoustic sensor network. Volunteers on the  <a href="https://zooniverse.org">Zooniverse</a> citizen science platform tagged the presence of 23 classes that were chosen in consultation with the New York City Department of Environmental Protection. These 23 fine-grained classes can be grouped into 8 coarse-grained classes. The recordings are split into three sets: training, validation, and test. The training and validation sets are disjoint with respect to the sensor from which each recording came, and the test set is displaced in time. For increased reliability, three volunteers annotated each recording. In addition, members of the SONYC team subsequently created a subset of verified, ground-truth tags using a two-stage annotation procedure in which two annotators independently tagged and then collectively resolved any disagreements. This subset of recordings with verified annotations intersects with all three recording splits. All of the recordings in the test set have these verified annotations.  In v2 version of this dataset, we have also included coarse spatiotemporal context information to aid in tag prediction when time and location is known. For more details on the motivation and creation of this dataset see the <a href="http://dcase.community/challenge2020/task-urban-sound-tagging-with-spatiotemporal-context">DCASE 2020 Urban Sound Tagging with Spatiotemporal Context Task website</a>.   Audio data The provided audio has been acquired using the SONYC acoustic sensor network for urban noise pollution monitoring. Over 60 different sensors have been deployed in New York City, and these sensors have collectively gathered the equivalent of over 50 years of audio data, of which we provide a small subset. The data was sampled by selecting the nearest neighbors on VGGish features of recordings known to have classes of interest. All recordings are 10 seconds and were recorded with identical microphones at identical gain settings. To maintain privacy, we quantized the spatial information to the level of a city block, and we quantized the temporal information to the level of an hour. We also limited the occurrence of recordings with positive human voice annotations to one per hour per sensor.   Label taxonomy The label taxonomy is as follows: <ol> <li>engine 1: small-sounding-engine 2: medium-sounding-engine 3: large-sounding-engine X: engine-of-uncertain-size</li> <li>machinery-impact 1: rock-drill 2: jackhammer 3: hoe-ram 4: pile-driver X: other-unknown-impact-machinery</li> <li>non-machinery-impact 1: non-machinery-impact</li> <li>powered-saw 1: chainsaw 2: small-medium-rotating-saw 3: large-rotating-saw X: other-unknown-powered-saw</li> <li>alert-signal 1: car-horn 2: car-alarm 3: siren 4: reverse-beeper X: other-unknown-alert-signal</li> <li>music 1: stationary-music 2: mobile-music 3: ice-cream-truck X: music-from-uncertain-source</li> <li>human-voice 1: person-or-small-group-talking 2: person-or-small-group-shouting 3: large-crowd 4: amplified-speech X: other-unknown-human-voice</li> <li>dog 1: dog-barking-whining</li> </ol> The classes preceded by an <code>X</code> code indicate when an annotator was able to identify the coarse class, but couldn’t identify the fine class because either they were uncertain which fine class it was or the fine class was not included in the taxonomy. <code>dcase-ust-taxonomy.yaml</code> contains this taxonomy in an easily machine-readable form.   Data splits This release contains a training subset (13538 recordings from 35 sensors), and validation subset (4308 recordings from 9 sensors), and a test subset (669 recordings from 48 sensors). The training and validation subsets are disjoint with respect to the sensor from which each recording came. The sensors in the test set will not disjoint from the training and validation subsets, but the test recordings are displaced in time, occurring after any of the recordings in the training and validation subset. The subset of recordings with verified annotations (1380 recordings) intersects with all three recording splits.  All of the recordings in the test set have these verified annotations.   Annotation data The annotation data are contained in <code>annotations.csv</code>, and encompass the training, validation, and test subsets. Each row in the file represents one multi-label annotation of a recording—it could be the annotation of a single citizen science volunteer, a single SONYC team member, or the agreed-upon ground truth by the SONYC team (see the annotator_id column description for more information).  Note that since the SONYC team members annotated each class group separately, there may be multiple annotation rows by a single SONYC team annotator for a particular audio recording.     Columns split The data split. (train, validate, test) sensor_id The ID of the sensor the recording is from. audio_filename The filename of the audio recording annotator_id The anonymous ID of the annotator. If this value is positive, it is a citizen science volunteer from the Zooniverse platform. If it is negative, it is a SONYC team member. If it is <code>0</code>, then it is the ground truth agreed-upon by the SONYC team. year The year the recording is from. week The week of the year the recording is from. day The day of the week the recording is from, with Monday as the start (i.e. <code>0</code>=Monday). hour The hour of the day the recording is from borough The NYC borough in which the sensor is located (<code>1</code>=Manhattan, <code>3</code>=Brooklyn, <code>4</code>=Queens). This corresponds to the first digit in the 10-digit NYC parcel number system known as Borough, Block, Lot (BBL). block The NYC block in which the sensor is located. This corresponds to digits 2—6 digit in the 10-digit NYC parcel number system known as Borough, Block, Lot (BBL). latitude The latitude coordinate of the block in which the sensor is located. longitude The longitude coordinate of the block in which the sensor is located. <coarse_id>-<fine_id>_<fine_name>_presence Columns of this form indicate the presence of fine-level class. <code>1</code> if present, <code>0</code> if not present. If <code>-1</code>, then the class was not labeled in this annotation because the annotation was performed by a SONYC team member who only annotated one coarse group of classes at a time when annotating the verified subset. <coarse_id>_<coarse_name>_presence Columns of this form indicate the presence of a coarse-level class. <code>1</code> if present, <code>0</code> if not present. If <code>-1</code>, then the class was not labeled in this annotation because the annotation was performed by a SONYC team member who only annotated one coarse group of classes at a time when annotating the verified subset. These columns are computed from the fine-level class presence columns and are presented here for convenience when training on only coarse-level classes. <coarse_id>-<fine_id>_<fine_name>_proximity Columns of this form indicate the proximity of a fine-level class. After indicating the presence of a fine-level class, citizen science annotators were asked to indicate the proximity of the sound event to the sensor. Only the citizen science volunteers performed this task, and therefore this data is not included in the verified annotations. This column may take on one of the following four values: (<code>near</code>, <code>far</code>, <code>notsure</code>, <code>-1</code>). If <code>-1</code>, then the proximity was not annotated because either the annotation was not performed by a citizen science volunteer, or the citizen science volunteer did not indicate the presence of the class.   Conditions of use Dataset created by Mark Cartwright, Jason Cramer, Ana Elisa Mendez Mendez, Yu Wang, Ho-Hsiang Wu, Vincent Lostanlen, Magdalena Fuentes, Graham Dove, Charlie Mydlarz, Justin Salamon, Oded Nov, and Juan Pablo Bello The SONYC-UST dataset is offered free of charge under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license: <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a> The dataset and its contents are made available on an “as is” basis and without warranties of any kind, including without limitation satisfactory quality and conformity, merchantability, fitness for a particular purpose, accuracy or completeness, or absence of errors. Subject to any liability that may not be excluded or limited by law, New York University is not liable for, and expressly excludes all liability for, loss or damage however and whenever caused to anyone by any use of the SONYC-UST dataset or any part of it.   Feedback Please help us improve SONYC-UST by sending your feedback to: <ul> <li>Mark Cartwright: <a href="mailto:mcartwright@gmail.com">mcartwright@gmail.com</a></li> </ul> In case of a problem, please include as many details as possible.   Acknowledgments We would like to thank all the Zooniverse volunteers who continue to contribute to our project. This work is supported by <a href="https://www.nsf.gov/awardsearch/showAward?AWD_ID=1544753">National Science Foundation award 1544753</a>.   Change log <ul> <li>2.3 Added the ground truth annotations for the test set, and regrouped the audio files for upload to Zenodo.</li> <li>2.2 Added the audio for the test set (audio-eval.tar.gz).</li> <li>2.1 The DCASE 2020 development dataset. 14778 new recordings added along with coarse spatiotemporal context information.</li> <li>1.0 Data is the same as v0.4. Publication added to README.</li> <li>0.4 Fixed error in annotations. Previously, the coarse class "machinery-impact" was accidentally indicated as present whenever "non-machinery-impact" was present regardless of the presence of "machinery-impact". This error has been fixed.</li> <li>0.3 Test set annotations added</li> <li>0.2 Test set audio files added</li> </ul>

opencc-by-4.0Sep 2019View details →

MOLI: multi-omics late integration with deep neural networks for drug response prediction

Harmonized data used in "MOLI: multi-omics late integration with deep neural networks for drug response prediction", 2019, Bioinformatics <a href="https://academic.oup.com/bioinformatics/article/35/14/i501/5529255">https://academic.oup.com/bioinformatics/article/35/14/i501/5529255</a>.  CNA.tar.gz contains CNA profiles with non-integer estimates of copy number, e.g. log-ratios. Please use binarized CNA profiles (CNA_binary.tar.gz) to replicate the results described in the paper.  All raw data were obtained from open sources: - https://www.cancerrxgene.org/ - ArrayExpress https://www.ebi.ac.uk/arrayexpress/ - Firehose Broad GDAC http://gdac.broadinstitute.org/runs/stddata__2016_01_28/data/ - Supplementary of Gao et al., 2015 https://www.nature.com/articles/nm.3954 Gene symbols were mapped to Entrez Gene IDs. Data preprocessing is described in detail in supplementary materials. The code is available at <a href="https://github.com/hosseinshn/MOLI/tree/master/preprocessing_scr">https://github.com/hosseinshn/MOLI/tree/master/preprocessing_scr</a>.

opencc-by-4.0Sep 2020View details →

Norwegian UV Network - minute data

Data are free for scientific, non-commercial purposes, but acknowledgement shall be given to DSA and NILU. Doserates at 1 minute resolution, based on real-sky measurements from GUV-instruments located at 10 sites in Norway (no substitution of gap periods). Calibrations based on the FARIN2005-campaign and annual site visits with a travelling reference GUV instrument. <a href="http://onlinelibrary.wiley.com/doi/10.1029/2007JD009731/abstract">http://onlinelibrary.wiley.com/doi/10.1029/2007JD009731/abstract</a> Groups of station-instruments have participated in 6 QASUME-campaigns (2003, 2005, 2009, 2010, 2014 and 2019). Interquartile range of UVI-measurements, relative to QASUME for the periode 2003-2019: +/-5 percent. Funding: Norwegian Environment Agency and Norwegian Ministry of Health and Care Services

opencc-by-4.0Sep 2020View details →

Approximation of a marine ecosystem model by artificial neural networks designed using a genetic algorithm

Data from the Paper: Approximation of a marine ecosystem model by artificial neural networks designed using a genetic algorithm. Abstract:  Marine ecosystem models are important to identify the  processes that affects for example the global carbon cycle. Computation of an annually periodic solution (i.e., a steady annual cycle) for these models requires a high computational effort. To reduce this effort, we approximated an exemplary marine ecosystem model by different artificial neural networks. We used a fully connected network, then applied the sparse evolutionary training  (SET) procedure, and finally applied a genetic algorithm (GA) to optimize both the   network topology. With all three approaches, a direct approximation of the  steady annual cycle  was not sufficiently accurate. However, using the mass-corrected prediction of the ANN as initial concentration for additional model runs, the results were in very good agreement.   In this way, we achieved a runtime reduction by about 15 \%. The result from the SET algorithm were comparable to those of the full network. Further application of the GA may lead to an even higher reduction. Content: Database sqlite <a href="https://zenodo.org/api/files/669d208b-7304-4d31-a6cc-3ad78e3544e9/ANN_Database.db">ANN_Database.db</a> zip-files with data:  <a href="https://zenodo.org/api/files/669d208b-7304-4d31-a6cc-3ad78e3544e9/ANN-Data.zip">ANN-Data.zip</a> structure and weights of used networks <a href="https://zenodo.org/api/files/669d208b-7304-4d31-a6cc-3ad78e3544e9/ANN-Results.zip">ANN-Results.zip</a> results obtained with networks <a href="https://zenodo.org/api/files/669d208b-7304-4d31-a6cc-3ad78e3544e9/Reference-Results.zip">Reference-Results.zip</a> reference results and training data

opencc-by-4.0Sep 2020View details →

data set related to article Brain Network Organization Correlates with Autistic Features in Preschoolers with Autism Spectrum Disorders and in Their Fathers - Preliminary Data from a DWI Analysis

This record contains raw data related to article Brain Network Organization Correlates with Autistic Features in Preschoolers with Autism Spectrum Disorders and in Their Fathers - Preliminary Data from a DWI Analysis

opencc-by-4.0Jul 2020View details →

TeamUp5G: A Multidisciplinary Approach to Training and Research on New RAN Techniques for 5G Ultra-Dense Mobile Networks

This video presents a summary of the main research directions being followed in TeamUp5G European Training Network. This project is teaming up a new generation of researchers and entrepreneurs ready to address complex engineering problems and innovation to work both at university and industry in the 5G field. Research is focused on the radio access network (RAN) techniques for 5G, considering ultra-dense mobile networks as a key ingredient of the mobile networks and their evolution. It covers a wide spread of topics from the physical layer and medium access control to applications,  looking at spectrum sharing and energy efficiency as important features.

opencc-by-4.0Oct 2020View details →

Dataset related to 'Pedagogy matters: Experience-dependent spatiotemporal brain network dynamics'

System Diversity (SD) and Spatio-Temporal Diversity (STD) values (as defined in [Vohryzek2020]) estimated on two groups of children (6-12 y.o.) from Montessori and traditional schools. We provide the values for the two groups and values from 1000 permutations between groups.

opencc-zeroOct 2020View details →

Networks of physiological adjustments and defenses, and their synergy with sodium (Na+) homeostasis explain the hidden variation for salinity tolerance across the cultivated Gossypium hirsutum germplasm

The abilities to mobilize and/or sequester excess ions within and outside the plant cell are important components of salt-tolerance mechanisms. Mobilization and sequestration of Na+ involves three transport systems facilitated by the plasma membrane H+/Na+ antiporter (SOS1), vacuolar H+/Na+ antiporter (NHX1), and Na+/K+ transporter in vascular tissues (HKT1). Many of these mechanisms are conserved across the plant kingdom. While Gossypium hirsutum (upland cotton) is significantly more salt-tolerant relative to other crops, the critical factors contributing to the phenotypic variation hidden across the germplasm have not been fully unraveled. In this study, the spatio-temporal patterns of Na+ accumulation along with other physiological and biochemical interactions were investigated at different severities of salinity across a meaningful genetic diversity panel across cultivated upland Gossypium. The aim was to define the importance of holistic or integrated effects relative to the direct effects of Na+ homeostasis mechanisms mediated by GhHKT1, GhSOS1, and GhNHX1. Multi-dimensional physio-morphometric attributes were investigated in a systems-level context using univariate and multivariate statistics, randomForest, and path analysis. Results showed that mobilized or sequestered Na+ contributes significantly to the baseline tolerance mechanisms. However, the observed variance in overall tolerance potential across a meaningful diversity panel were more significantly attributed to antioxidant capacity, maintenance of stomatal conductance, chlorophyll content, and divalent cation (Mg2+) contents other than Ca2+ through a complex interaction with Na+ homeostasis. The multi-tier macro-physiological, biochemical and molecular data generated in this study, and the networks of interactions uncovered strongly suggest that a complex physiological and biochemical synergy beyond the first-line-of defense (Na+ sequestration and mobilization) accounts for the total phenotypic variance across the primary germplasm of Gossypium hirsutum. These findings are consistent with the recently proposed Omnigenic Theory for quantitative traits and should contribute to a modern look at phenotypic selection for salt tolerance in cotton breeding.

opencc-zeroOct 2020View details →

Pollen transfer networks reveal alien species as main heterospecific pollen donors with fitness consequences for natives

<ol> <li>The ecological dynamics of co-flowering communities are largely mediated by pollinators. However, current understanding of pollinator-mediated interactions primarily relies on how co-flowering plants influence attraction of shared pollinators, and much less is known about plan-plant interactions that occur via heterospecific pollen (HP) transfer. Invaded communities in particular can be highly affected by the transfer of alien pollen, but the strength, drivers, and fitness consequences of these interactions at a community scale are not well understood.</li> <li>Here we analyze HP transfer networks in nine coastal communities in the Yucatan Mexico that vary in the relative abundance of invasive flowers to evaluate how HP donation and receipt varies between native and alien plants. We further evaluate whether HP donation and receipt are mediated by floral traits (e.g. display, flower size) or pollinator visitation rate. Finally, we evaluated whether post-pollination success (proportion of pollen tubes produced) was affected by alien HP receipt and whether the effect varied between native and alien recipients.</li> <li>HP transfer networks exhibit relatively high connectance (ca. 15%), suggesting high HP transfer within the studied communities. Significant network nestedness further suggests the existence of species that predominantly act as HP donors or recipients in the community. Species-level analyses showed that natives receive 80% more HP compared to alien species, and that alien plants donate 40% more HP than natives. HP receipt and donation were mediated by different floral traits and such effects were independent of plant origin (native or alien). The proportion of alien HP received significantly affected conspecific pollen tube success in natives, but not that of alien species.</li> <li> Synthesis. Our results suggest that HP transfer in invaded communities is widespread, and that native and alien species play different roles within HP transfer networks, which are mediated by a different suite of floral traits. Alien species, in particular, play a central role as HP donors and are more tolerant to HP receipt than natives—a finding that points to two overlooked mechanisms facilitating alien plant invasion and success within native co-flowering communities.</li> </ol>