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1,721 results for “network data”
Data from: Insights into the ecology and evolution of polyploid plants through network analysis
Polyploidy is a widespread phenomenon throughout eukaryotes, with important ecological and evolutionary consequences. Although genes operate as components of complex pathways and networks, polyploid changes in genes and gene expression have typically been evaluated as either individual genes or as a part of broad-scale analyses. Network analysis has been fruitful in associating genomic and other 'omic'-based changes with phenotype for many systems. In polyploid species, network analysis has the potential not only to facilitate a better understanding of the complex 'omic' underpinnings of phenotypic and ecological traits common to polyploidy, but also to provide novel insight into the interaction among duplicated genes and genomes. This adds perspective to the global patterns of expression (and other 'omic') change that accompany polyploidy and to the patterns of recruitment and/or loss of genes following polyploidization. While network analysis in polyploid species faces challenges common to other analyses of duplicated genomes, present technologies combined with thoughtful experimental design provide a powerful system to explore polyploid evolution. Here, we demonstrate the utility and potential of network analysis to questions pertaining to polyploidy with an example involving evolution of the transgressively superior cotton fibres found in polyploid Gossypium hirsutum. By combining network analysis with prior knowledge, we provide further insights into the role of profilins in fibre domestication and exemplify the potential for network analysis in polyploid species.
Data from: Elements of metacommunity structure of diatoms and macroinvertebrates within stream networks differing in environmental heterogeneity
<p><strong>Aim:</strong> Idealized metacommunity structures (i.e. checkerboard, random, quasi-structures, nested, Clementsian, Gleasonian, and evenly spaced) have recently gained increasing attention, but their relationships with environmental heterogeneity and how they vary with organism groups remain poorly understood. Here we tested two main hypotheses: (1) gradient-driven patterns (Clementsian and Gleasonian) occur frequently in heterogeneous environments, and (2) small organisms (here, diatoms) are more likely to exhibit gradient-driven patterns than large organisms (here, macroinvertebrates).</p> <p><strong>Location:</strong> Streams in three regions in China.</p> <p><strong>Taxon:</strong> Diatoms and macroinvertebrates.</p> <p><strong>Methods:</strong> The stream diatom and macroinvertebrate data, as well as the environmental data collected from the same set of sites were used to examine the idealized metacommunity structures via the elements of the metacommunity structure (EMS; coherence, turnover, and boundary clumping) analysis in three regions. We extended the traditional EMS approach by ordering sites along known environmental gradients.</p> <p><strong>Results: </strong>We found that Clementsian structure with high degrees of coherence and turnover, and significantly positive clumping was typically observed in the high-heterogeneity regions, whereas randomness was prevalent in the low-heterogeneity region. Macroinvertebrates exhibited clearer Clementsian structures compared with diatoms, while diatoms showed more randomness compared with macroinvertebrates, indicating a stronger role of environmental filtering for macroinvertebrates than diatoms. In most cases, the results of the more novel EMS approach differed from the results of the traditional EMS technique.</p> <p><strong>Main Conclusions:</strong> Our results suggested that the occurrence of different metacommunity structures may be related with the degree of regional environmental heterogeneity. However, diatom metacommunities were more random than those of macroinvertebrate, and such an unexpected result may result from different dispersal abilities between the two organism groups. In addition, we found that the novel EMS approach increased power in discerning metacommunity structure in comparison to the traditional EMS technique.</p>
Figure 8 from: Smirnova L, Mergen P, Groom Q, De Wever A, Penev L, Stoev P, Pe'er I, Runnel V, Camacho A, Vincent T, Agosti D, Arvanitidis C, Bonet F, Saarenmaa H (2016) Data sharing tools adopted by the European Biodiversity Observation Network Project. Research Ideas and Outcomes 2: e9390. https://doi.org/10.3897/rio.2.e9390
Figure 8 - The patchiness of survey coverage in Europe illustrated by the distribution map of Plantago lanceolata taken from GBIF in 2016. This species is one of the commonest and most widespread in Europe, it should occur in almost all areas of this map, but in fact the data traces out the borders of countries and area who have published data on GBIF.
Figure 9 from: Smirnova L, Mergen P, Groom Q, De Wever A, Penev L, Stoev P, Pe'er I, Runnel V, Camacho A, Vincent T, Agosti D, Arvanitidis C, Bonet F, Saarenmaa H (2016) Data sharing tools adopted by the European Biodiversity Observation Network Project. Research Ideas and Outcomes 2: e9390. https://doi.org/10.3897/rio.2.e9390
Figure 9 - Information flows between EU BON and LTER Europe, as envisaged on the 3rd EU BON Stakeholder Roundtable in Granada on 9-11 December 2015.
Figure 6 from: Smirnova L, Mergen P, Groom Q, De Wever A, Penev L, Stoev P, Pe'er I, Runnel V, Camacho A, Vincent T, Agosti D, Arvanitidis C, Bonet F, Saarenmaa H (2016) Data sharing tools adopted by the European Biodiversity Observation Network Project. Research Ideas and Outcomes 2: e9390. https://doi.org/10.3897/rio.2.e9390
Figure 6 - Individual Metacat instances can be connected to DataOne which replicates public files. Thus the data is still available if a single instance goes offline. https://search.dataone.org/#data/page/0
Figure 5 from: Smirnova L, Mergen P, Groom Q, De Wever A, Penev L, Stoev P, Pe'er I, Runnel V, Camacho A, Vincent T, Agosti D, Arvanitidis C, Bonet F, Saarenmaa H (2016) Data sharing tools adopted by the European Biodiversity Observation Network Project. Research Ideas and Outcomes 2: e9390. https://doi.org/10.3897/rio.2.e9390
Figure 5 - PPBio has installed a Metacat instance for their researchers to upload and make publicly available the results of work related to biodiversity in the Western Amazon. https://ppbiodata.inpa.gov.br/metacatui/
Figure 3 from: Smirnova L, Mergen P, Groom Q, De Wever A, Penev L, Stoev P, Pe'er I, Runnel V, Camacho A, Vincent T, Agosti D, Arvanitidis C, Bonet F, Saarenmaa H (2016) Data sharing tools adopted by the European Biodiversity Observation Network Project. Research Ideas and Outcomes 2: e9390. https://doi.org/10.3897/rio.2.e9390
Figure 3 - The implementation of Darwin Core Archive in Plazi to transfer treatment data. Observation data described with Darwin Core terms.
Figure 4 from: Smirnova L, Mergen P, Groom Q, De Wever A, Penev L, Stoev P, Pe'er I, Runnel V, Camacho A, Vincent T, Agosti D, Arvanitidis C, Bonet F, Saarenmaa H (2016) Data sharing tools adopted by the European Biodiversity Observation Network Project. Research Ideas and Outcomes 2: e9390. https://doi.org/10.3897/rio.2.e9390
Figure 4 - The public data repository provided by the Knowledge Network for Biocomplexity (KNB). https://knb.ecoinformatics.org/#data/page/0
Figure 1 from: Smirnova L, Mergen P, Groom Q, De Wever A, Penev L, Stoev P, Pe'er I, Runnel V, Camacho A, Vincent T, Agosti D, Arvanitidis C, Bonet F, Saarenmaa H (2016) Data sharing tools adopted by the European Biodiversity Observation Network Project. Research Ideas and Outcomes 2: e9390. https://doi.org/10.3897/rio.2.e9390
Figure 1 - ARPHA consists of two integrated workflows: in ARPHA-XML, the manuscript is written and processed via the ARPHA Writing Tool, and in ARPHA-DOC, the manuscript is submitted and processed as document file(s).
Data release for the Open Biomedical Network Benchmark
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Data set for Finding Equivalence of Layout Independent Water Distribution Network for Expansion/Reorganization Using Nonlinear Multi-Port Thevenin Theorem
<p>This is the dataset for our manuscript titled "Finding Equivalence of Layout Independent Water Distribution Network for Expansion/Reorganization Using Nonlinear Multi-Port Thevenin Theorem".</p>
Data Repository for "Integrating Water Quality Data with a Bayesian Network Model to Improve Spatial and Temporal Phosphorus Attribution: Application to the Maumee River Basin"
<p>Data for "Integrating Water Quality Data with a Bayesian Network Model to Improve Spatial and Temporal Phosphorus Attribution: Application to the Maumee River Basin". This repository contains all the processed data used in the simulation (in "processed" folder), part of the raw data (in "raw" folder), and the SWAT simulation results (in "SWAT" folder). The code for processing the raw data, which are either provided here or publicly available online, is provided in the <a href="https://doi.org/10.5281/zenodo.8132662">code repository</a>. The links to the publicly available raw data are also provided in the code repository.</p>
Data from: Trade-offs in Coordination Strategies for Networked Jazz Performances (anonymized for peer review)
<p>This dataset is associated with the paper “Trade-offs in Coordination Strategies for Networked Jazz Performances” and includes recordings of improvisations by jazz duos over a network.</p> <p><strong>Introduction:</strong></p> <p>This dataset includes data from approximately four hours of live, improvised musical duo performances over a simulated network environment collected in LOCATION REMOVED FOR PEER REVIEW. Data includes audio and video recordings of 130 individual performances, biometric data, and subjective evaluations and comments from the musicians. The primary aim of the project was to collect data via a novel performance capture and manipulation system for use in the empirical modelling of ensemble coordination strategies during networked music-making. This analysis is reported in ANONYMIZED FOR PEER REVIEW. Please refer to this publication for full details on the data collection procedure.</p> <p>The ten musicians shown in these recordings were recruited for their expertise in jazz improvisation. They were grouped into five duos consisting each of one pianist and drummer, with no musician performing in more than one duo. Participants were instructed to improvise together over a standard twelve-bar blues musical structure, but following a formula which required them to provide a clear and unambiguous pulse of continuous quarter notes. Varying amounts of network latency and jitter were simulated for each performance, consisting respectively of the minimum amount of delay applied to the live feedback a musician heard from their partner and the degree that this delay varied. The amount of latency and jitter applied to the performance is summarised in the file or directory name for each performance and is described in detail in the above publication. Note that latency and jitter conditions were presented in a random order for each duo.</p> <p><strong>Data collected includes:</strong></p> <ul> <li>audio recordings for each performance, with and without delay, collected via direct line-in (MIDI, WAV).</li> <li>video recordings, collected via high-quality webcams (MKV, AVI).</li> <li>streams of the quarter note pulse provided by each musician in a performance (MIDI).</li> <li>muxed audio-visual recordings of both participants in each performance (MP4)</li> <li>accelerometer and photoplethysmography streams, collected from arm-worn devices (TXT, duos 3-5 only)</li> <li>questionnaire responses from performers, evaluating each condition (XLSX)</li> <li>ratings of performance quality from an unbiased sample of listeners, collected during an online perceptual study (CSV)</li> </ul> <p><strong>Repository structure:</strong></p> <p><strong><em>NB: please see this section of the code documentation website (LINK REMOVED FOR PEER REVIEW) for a full description of how to recreate the analyses and models created in the paper.</em></strong></p> <p>The files <em>data.zip </em>and <em>data.z0*</em> contain all data collected from the study, APART from the perceptual study stimuli & results. To open these files, download the <em>data.zip</em> file and <em><strong>all the corresponding volumes ending in .z0 </strong></em>and open the <em>data.zip</em> file using a tool for opening multi-part zip files, such as WinRAR. <em>Do not try to open the files ending in .z0</em>, otherwise you may get a message about the data being corrupted. Inside <em>data.zip</em>, you'll see the following folders and files:</p> <ul> <li><em>avmanip_output</em>: the raw MIDI, audio, and video output from each performance <ul> <li>the subfolders are organised with a single folder per participant duo, experimental block, and condition.</li> <li>avmanip_output\trial_1\Block 1\Condition 1 - 23 05 relates to the performance of the first duo of participants in the first session of the experiment, in the first condition they encountered, with 23ms of latency and 0.5x jitter.</li> </ul> </li> <li><em>midi_bpm_cleaning</em>: the cleaned MIDI files (quarter note onset positions) <ul> <li>the subfolders are organised similarly to the <em>avmanip_output</em> folder, using the same conventions.</li> </ul> </li> <li><em>muxed_performances</em>: the combined audio-video .mp4 files from each performance <ul> <li>these files are labelled in the format: duo_session_latency_jitter_keysfmt_drumsfmt.</li> <li>muxed_performances\kdelay_ddelay\d1_s1_l23_j00_kdelay_ddelay.mp4 relates to the performance of the first duo of participants in the first session of the experiment, in the first condition they encountered, with 23ms of latency and 0.5x jitter, and with latency and jitter applied to both keys and drummer.</li> <li>for more information on recreating these videos, see the linked section of the code documentation website (LINK REMOVED FOR PEER REVIEW).</li> </ul> </li> <li><em>questionnaire_anonymized</em>: the anonymized questionnaire responses given by participants, also contained in the supplementary material of the associated paper (see preprint).</li> </ul> <p>Alongside <em>data.zip </em>and the <em>data.z0*</em> archives, there are two further loose files, <em>Database View Participant - Dashboard.csv, Database View SuccessTrial - Dashboard.csv, </em>which are the anonymized demographic and response data from the perceptual experiment, and one loose archive folder <em>perceptual_study_videos.rar</em>, which contains the stimuli used in the perceptual experiment.</p> <p><strong>Usage:</strong></p> <p>To reproduce the analysis, models, and graphs from the paper, download the code in code-analysis-modeling.rar and extract it to a new folder, then extract all the files inside <em>data.zip</em> and the two perceptual study CSV files into \data\raw. Install Python 3.10 if you don't have it already, and then open a command prompt in the root directory of the analysis code and execute the command <em>run.cmd</em><em>.</em></p> <p>The remaining code files (<em>code-perceptual-study.rar</em> and <em>code-testbed-software.rar</em>) contain the code used in the perceptual and laboratory experiments. Documentation and installation instructions are provided within each archive.</p> <p>These recordings of live, improvised duo performances are unattributed and anonymized as agreed with participants at the point of data collection. The musicians involved received a one-off, fixed payment for their time and had their travel expenses reimbursed, with funding provided by ANONYMIZED FOR PEER REVIEW. All participants consented to the use of their recordings for projects by the current authors and for these recordings to be shared with interested members of the music psychology community, with the intention of furthering academic research. The musicians did not intend that the recordings be used for commercial, artistic, or entertainment purposes, and such use is not permitted.</p> <p><strong>Citation:</strong></p> <p>If you use this dataset in your research, please follow the citation format posted on GitHub (LINK REMOVED FOR PEER REVIEW).</p> <p><strong>Contact:</strong></p> <p>ANONYMIZED FOR PEER REVIEW</p>
Supporting data files: Exploring the early molecular pathogenesis of osteoarthritis using differential network analysis of human synovial fluid
<p><strong>SS-218412_v4.1_other.hybNorm.medNormInt.plateScale.medNormSMP.adat</strong></p> <p>Main raw data file - normalized aptamer abundances</p> <p><strong>m4.L1_0.1_L2_0.001_ITER_10K_STABILITYSS_1K.rds</strong></p> <p>R data file object - GGM model output used in manuscript</p>
Dopant network processing units as tuneable extreme learning machines - Data Sheet 1
<p>Inspired by the highly efficient information processing of the brain, which is based on the chemistry and physics of biological tissue, any material system and its physical properties could in principle be exploited for computation. However, it is not always obvious how to use a material system’s computational potential to the fullest. Here, we operate a dopant network processing unit (DNPU) as a tuneable extreme learning machine (ELM) and combine the principles of artificial evolution and ELM to optimise its computational performance on a non-linear classification benchmark task. We find that, for this task, there is an optimal, hybrid operation mode (“tuneable ELM mode”) in between the traditional ELM computing regime with a fixed DNPU and linearly weighted outputs (“fixed-ELM mode”) and the regime where the outputs of the non-linear system are directly tuned to generate the desired output (“direct-output mode”). We show that the tuneable ELM mode reduces the number of parameters needed to perform a formant-based vowel recognition benchmark task. Our results emphasise the power of analog in-matter computing and underline the importance of designing specialised material systems to optimally utilise their physical properties for computation.</p>
Data associated with Balasubramaniam, Beisner et al. (PeerJ, 2016): "Social buffering and contact transmission: Network connections have beneficial and detrimental effects on Shigella infection risk among captive rhesus macaques"
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Data from: A global network of marine protected areas for food
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Data from: Family network size and survival across the lifespan of female macaques
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Data from: Offspring social network structure predicts fitness in families
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Data from: System-level insights into the cellular interactome of a non-model organism: inferring, modelling and analysing functional gene network of Soybean (Glycine max)
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ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.