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24 results for “stream processing”
Terrestrial-Stream Biodiversity Litter Processing Datasets from Watershed 20 within the Coweeta Hydrologic Laboratory
Although litter decomposition is a fundamental ecological process, most of our understandings comes from studies of single-species decay. Recently, litter-mixing studies have tested whether monoculture data can be applied to mixed-litter systems. These studies have mainly attempted to detect non-additive effects of litter mixing, which address potential consequences of random species loss -- the focus is not on which species are lost, but the decline in diversity per se. Under global change, species loss is likely to be non-random, with some species more vulnerable to extinction than others. Under such scenarios, the effects of individual species (additivity) as well as of species interactions (non-additivity) on decomposition rates are of interest. To examine potential impacts of non-random species loss on ecosystems, we studied additive and non-additive effects of litter mixing on decomposition. A full-factorial litterbag experiment was conducted using four deciduous leaf species, from which mass loss and nitrogen content were measured. This study was conducted at the Coweeta Hydrologic Laboratory in Watershed 20 on Ball Creek that drains into Coweeta Creek, a tributary of the Little Tennessee River. Data were analyzed using a statistical approach that first looks for additive identiy effects based on the presence or absence of species and then significant species interactions occurring beyond those. It partitions non-additive effects into those caused by richness and/ or composition. This approach addresses questions key to understanding the potential effects of species loss on ecosystem processes. If additive effects dominate, the consequences for decomposition dynamics will be predictable based on our knowledge of individual species, but not statistically predictable if non-additive effects dominate.
STREAMS Project: Emergent landscape patterns in stream ecosystem processes resulting from groundwater/surface water interactions
This Data Set is hosted by the Luquillo LTER Program (LUQ) and owned by a LUQ's investigator. Our primary objective is to understand the linkage between surface-subsurface water interactions and ecosystem processes in neotropical lowland streams over an extended time frame (>25 yrs). Proposed research will occur at La Selva Biological Reserve in Costa Rica, which is owned and operated by the Organization for Tropical Studies In tectonically active regions of Central America, it is common for solute-rich groundwater to emerge at gradient breaks within the complex volcanic topography of mountains and foothills which intergrade with the coastal plain. These groundwaters can significantly influence solute chemistry and related ecological and ecosystem-level processes in receiving surface waters. Many solute-rich groundwaters are associated with underlying volcanic activity which has altered the chemistry of receiving streams throughout Central America. Geothermally-modified groundwaters, surfacing at the gradient break between the Central Mountain range and the coastal plain at La Selva Biological Station, have high levels of P (up to 400 mg SRP L-1) and other solutes (Ca, Cl, Mg, SO4) but are not elevated in temperature. Spatial patterns in stream solute chemistry are determined by geomorphic features of the volcanic landscape that include: upland lavas drained by P-poor streams; a gradient break (~50 m.a.s.l.), at or near where P-rich springs emerge; and lowland alluvial areas drained by streams that are both P-rich and P-poor depending on whether they receive the input of solute-rich springs. Our project is the first to determine long-term effects of nutrient enrichment in a detrital-based stream within the wet tropics. We will continue to build upon our long-term(1988-present) data set on stream solute chemistry, which is the only one that we are aware of for lowland primary rainforest of Central America. The proposed project will build on 18 years of past resear
Code to reproduce the analyses of "Multiple stressors alter greenhouse gas concentrations in streams through local and distal processes"
<p>Streams are significant contributors of greenhouse gases (GHG) to the atmosphere, and the increasing number of stressors degrading freshwaters may exacerbate this process, posing a threat to climatic stability. However, it is unclear whether the influence of multiple stressors on GHG concentrations in streams results from increases of in-situ metabolism (i.e., local processes) or from changes in upstream and terrestrial GHG production (i.e., distal processes). Here, we hypothesize that the mechanisms controlling multiple stressor effects vary between <span>carbon dioxide (</span>CO<sub>2</sub>) and <span>methane (</span>CH<sub>4</sub>), with the latter being more influenced by changes in local stream metabolism, and the former mainly responding to distal processes. To test this hypothesis, we measured stream metabolism and the concentrations of CO<sub>2</sub> (<em>p</em>CO<sub>2</sub>) and CH<sub>4</sub> (<em>p</em>CH<sub>4</sub>) in 50 stream sites that encompass gradients of <span>nutrient enrichment, oxygen depletion, thermal stress, riparian degradation and discharge</span>. Our results indicate that these stressors had additive effects on stream metabolism and GHG concentrations, with stressor interactions explaining limited variance. Nutrient enrichment was associated with higher stream heterotrophy and <em>p</em>CO<sub>2</sub>, whereas <em>p</em>CH<sub>4</sub> increased with oxygen depletion and water temperature. Discharge was positively linked to primary production, respiration and heterotrophy but correlated negatively with <em>p</em>CO<sub>2.</sub> Our models indicate that CO<sub>2</sub>-equivalent concentrations can more than double in streams that experience high nutrient enrichment and oxygen depletion, as compared to those with oligotrophic and oxic conditions. Structural equation models revealed that the effects of nutrient enrichment and discharge on <em>p</em>CO<sub>2</sub> were related to distal processes rather than local metabolism. In contrast, <em>p</em>CH<sub>4</sub> responses to nutrient enrichment, discharge and temperature were related to both local metabolism and distal processes. Collectively, our study illustrates <span>potential climatic feedbacks resulting from freshwater degradation and </span>provides insight into the processes mediating stressor impacts on the production of GHG in streams.</p>
An Uncertainty-Aware Approach to Optimal Configuration of Stream Processing Systems
<p>The datasets in this release support the results presented in the paper</p> <blockquote> <p>P. Jamshidi, G. Casale, "An Uncertainty-Aware Approach to Optimal Configuration of Stream Processing Systems", accepted for presentation at MASCOTS 2016.</p> </blockquote> <p>An open access to the paper is available at https://arxiv.org/abs/1606.06543</p> <blockquote> <p>Also open source code is available at https://github.com/dice-project/DICE-Configuration-BO4CO</p> </blockquote> <p>The archive contains 10 comma separated datasets representing performance measurements (throughput and latency) for 3 different stream benchmark applications. These have been experimentally collected on 5 different cloud cluster over the course of 3 months (24/7). Each row in the datasets represents a different configuration setting for the application and the last two columns represent the average performance of the application measured over the course of 10 minutes under that specific configuration setting. The datasets contains a full factorial and exhaustive measurements for all possible settings limited to a predetermined interval for each variable. Each dataset is named in the following format: "<em>benchmark_application-dimensions-cluster_name</em>". For example, "wc-6d-c1" refers to WordCount benchmark application with 6 dimensions (i.e., we varied 6 configuration parameters) and the application was deployed on c1 cluster (OpenNebula, see Appendix). This resulted in a dataset of size 2880, i.e., it has taken 2880*10m=480h=20days for collecting the data! </p> <p>For more information about the data refer to the appendix of the paper: https://arxiv.org/abs/1606.06543. </p> <p>When referring to the dataset or code please cite the paper above.</p>
Replication Package for: Streaming vs. Functions: A Cost Perspective on Cloud Event Processing
<p>In cloud event processing, data generated at the edge is processed in real-time by cloud resources. Both distributed stream processing (DSP) and Function-as-a-Service (FaaS) have been proposed to implement such event processing applications. FaaS emphasizes fast development and easy operation, while DSP emphasizes efficient handling of large data volumes. Despite their architectural differences, both can be used to model and implement loosely-coupled job graphs. In this paper, we consider the selection of FaaS and DSP from a cost perspective. We implement stateless and stateful workflows from the Theodolite benchmarking suite using cloud FaaS and DSP. In an extensive evaluation, we show how application type, cloud service provider, and runtime environment can influence the cost of application deployments and derive decision guidelines for cloud engineers.</p>
Replication Package for: Benchmarking scalability of stream processing frameworks deployed as microservices in the cloud
<h2>Replication Package for: Benchmarking scalability of stream processing frameworks deployed as microservices in the cloud</h2><p>This is our replication package for our study on <i>Benchmarking scalability of stream processing frameworks deployed as microservices in the cloud</i>.</p><p>All scalability experiments are performed with the scalability benchmarking framework <a href="https://www.theodolite.rocks/">Theodolite</a> at <a href="https://www.se.informatik.uni-kiel.de/en/research/software-performance-engineering-lab-spel">Kiel University's Software Performance Engineering Lab (SPEL)</a> or Google Cloud.</p><p>With this replication package, we provide:</p><ul><li><a href="https://www.theodolite.rocks/concepts/benchmarks-and-executions.html">Benchmark execution files</a> in <i>executions</i>,</li><li>our benchmark (raw) results in <i>results</i>, and</li><li>analysis script for our results in <i>analysis</i>.</li></ul><h3>Repeating Benchmark Executions</h3><p>All our Theodolite executions are tailored to either the SPEL cluster or the Google Cloud.</p><h4>Kiel University's Software Performance Engineering Lab (SPEL)</h4><p>The SPEL cluster consists of 5 nodes, named <i>kube1-1</i> to <i>kube1-5</i> and labeled with <i>env=dev</i>. To run them in your local cluster, make sure to provide the same infrastructure or rename node selectors in the execution files accordingly.</p><p>To install Theodolite, run:</p><blockquote><p>helm install theodolite theodolite/theodolite --version 0.8.6 -f https://raw.githubusercontent.com/cau-se/theodolite/main/helm/preconfigs/extended-metrics.yaml -f se-cluster-dev.yaml</p></blockquote><p>or for the vertical scalability experiment:</p><blockquote><p>helm install theodolite theodolite/theodolite --version 0.8.6 -f https://raw.githubusercontent.com/cau-se/theodolite/main/helm/preconfigs/extended-metrics.yaml -f se-cluster-dev.yaml -f se-cluster-dev-vertical.yaml</p></blockquote><p>See <a href="https://www.theodolite.rocks">Theodolite's documentation</a> for further usage instructions.</p><h4>Google Cloud</h4><p>In the public cloud baseline experiments, the cluster consists of 5 e2-standard-32 nodes.</p><p>To install Theodolite, run:</p><blockquote><p>helm install theodolite theodolite/theodolite --version 0.8.6 -f https://raw.githubusercontent.com/cau-se/theodolite/main/helm/preconfigs/extended-metrics.yaml -f gcp-cluster-dev.yaml</p></blockquote><p>For the experiments testing higher load intensities, the cluster consists of 4 e2-standard-16 nodes labeled with <i>type=infra</i> and 4 or 8 e2-standard-16 nodes with label <i>type=sut</i>. To install Theodolite in this cluster, run:</p><blockquote><p>helm install theodolite theodolite/theodolite --version 0.8.6 -f https://raw.githubusercontent.com/cau-se/theodolite/main/helm/preconfigs/extended-metrics.yaml -f gcp-cluster-stress.yaml</p></blockquote><p>In both cases, change the maximum load generated per load generator instance:</p><blockquote><p># Generate max. 100000 rec/sec per load generator instance export MAX_RECORDS_PER_INSTANCE=100000 kubectl patch benchmarks uc1-beam-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc1-beam-samza --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc1-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc1-hazelcastjet --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc1-kstreams --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc2-beam-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc2-beam-samza --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc2-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc2-hazelcastjet --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc2-kstreams --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc3-beam-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc3-beam-samza --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc3-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc3-hazelcastjet --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc3-kstreams --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc4-beam-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc4-beam-samza --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc4-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc4-hazelcastjet --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc4-kstreams --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]"</p></blockquote><p>See <a href="https://www.theodolite.rocks">Theodolite's documentation</a> for further usage instructions.</p><h3>Repeating Results Analysis</h3><p>To inspect, repeat, or extend our results analysis, see <i>results</i> or run the corresponding notebooks in <i>analysis</i>.</p><p>For analyzing and visualizing benchmark results, either Docker or a Jupyter installation with Python 3.7 or 3.8 is required (e.g., in a virtual environment). Moreover, we require some Python libraries, which can be installed by:</p><blockquote><p>python3.8 -m venv .venv # source .venv/bin/activate pip install -r analysis/requirements.txt</p></blockquote><p> </p>
Riparian forests and macroinvertebrates support multiple ecosystem processes across temperate and tropical streams
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Data for: Climate change is poised to alter mountain stream ecosystem processes via organismal phenological shifts
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Replication Package for: Theodolite: Scalability Benchmarking of Distributed Stream Processing Engines in Microservice Architectures
<p>This repository contains a replication package and experimental results for our study <em>Theodolite: Scalability Benchmarking of Distributed Stream Processing Engines in Microservice Architectures</em>.</p> <p>The following description can also be found in the README.md file.</p> <p><strong>Repeating Benchmark Execution</strong></p> <p><em><strong>The following introduction describes how to repeat our scalability experiments. If you plan to conduct your own studies, we suggest to use the latest version of <a href="https://github.com/cau-se/theodolite">Theodolite</a> with significantly enhanced usability.</strong></em></p> <p>The Apache Kafka Streams scalability experiments of our study were executed with <a href="https://github.com/cau-se/theodolite/tree/v0.1.2">Theodolite v0.1.2</a>. To repeat our Kafka Streams experiments:</p> <ol> <li>Clone and install <a href="https://github.com/cau-se/theodolite/tree/v0.1.2">Theodolite v0.1.2</a> according to the official documentation located in <code>execution</code>.</li> <li>Copy the file <code>repeat-kstream.sh</code> into Theodolite's <code>execution</code> directory.</li> <li>Run the repetition file with <code>./repeat-kstream.sh</code> from within the <code>execution</code> directory.</li> </ol> <p>Our Apache Flink benchmark implementations are currently migrated to the latest version of Theodolite. <a href="https://github.com/cau-se/theodolite/tree/apache-flink">Theodolite's <code>apache-flink</code> Branch</a> provides the basis for our Flink scalability experiments. To repeat them:</p> <ol> <li>Clone <a href="https://github.com/cau-se/theodolite/tree/apache-flink">Theodolite's <code>apache-flink</code> Branch</a> and install Theodolite according to the official documentation located in <code>execution</code> (should be identical to the installation for Kafka Streams (see above)).</li> <li>Copy the files <code>repeat-flink-without-checkpointing.sh</code> and <code>repeat-flink-with-checkpointing.sh</code> into Theodolite's <code>execution</code> directory.</li> <li>Switch to the <code>execution</code> directory.</li> <li>Run the first repetition file with <code>./repeat-flink-with-checkpointing.sh</code>.</li> <li>Disable checkpointing by reconfiguring the Kubernetes resources <code>jobmanager-job.yaml</code> and <code>taskmanager-job-deployment.yaml</code> for each benchmark (<code>uc{1,2,3,4}-application</code>) by setting the environment variable <code>CHECKPOINTING</code> to <code>"false"</code>.</li> <li>Run the second repetition file with <code>./repeat-flink-without-checkpointing.sh</code>.</li> </ol> <p><em>Please note that the naming of our benchmarks recently changed. While our publication already uses the new naming, the corresponding Theodolite versions are is still using the old one. Specifically, this means that UC1 in the publication is UC1 in Theodolite, UC2 in the publication is UC3 in Theodolite, UC3 in the publication is UC4 in Theodolite, and UC4 in the publication is UC2 in Theodolite.</em></p> <p><strong>Raw Measurements</strong></p> <p>The results of above benchmark execution can be found in the <code>measurements</code> directory. These are CSV files, containing the measured lag trend over time for a certain subexperiment. Theodolite creates a bunch of additional files, which serve for debugging and preliminary interpretation. As these files are not required for replication, we do not included them in this package.</p> <p>The CSV files are named according to the schema <code>exp{id}_{uc}_{load}_{inst}_totallag.csv</code>, where <code>{id}</code> represents the experiment ID, assigned by Theodolite, <code>{uc}</code> the benchmark name, <code>{load}</code> the generated load, and <code>{inst}</code> the number of evaluated instances.</p> <p>The CSV table <code>experiments.csv</code> provides an overview about the configurations used in each experiment.</p> <p><strong>Reproducing Scalability Analysis</strong></p> <p><em><strong>The following introduction describes how to repeat our scalability analysis, either with our measurements or with your own. If you plan to conduct your own studies, we suggest to use the latest version of <a href="https://github.com/cau-se/theodolite">Theodolite</a> with significantly enhanced usability.</strong></em></p> <p>Analyzing the Theodolite's measurements is done using two Jupyter notebooks. In general, these notebooks should be runnable by any Jupyter server. Python 3.7 or 3.8 is required (e.g., in a virtual environment) as well as some Python libraries, which can be installed via: <code>pip install -r requirements.txt</code>. See the <a href="https://github.com/cau-se/theodolite/tree/master/analysis">Theodolite documentation</a> for additional installation guidance.</p> <p><strong>Obtaining a Scalability Graph as a CSV File</strong></p> <p>The <code>scalability-graph.ipynb</code> notebook combines the measurements (i.e., the <code>totallag.csv</code> files) of one experiment. It produces a CSV file, which provides a mapping of load intensities to minimum required resources for that load (i.e., the scalability graph). The CSV files are named according to the schema <code>exp{id}_min-suitable-instances.csv</code>, where <code>{id}</code> represents the experiment ID. Additional guidance is provided in the notebook.</p> <p><strong>Resulting Scalability Graph CSV Files</strong></p> <p>The <code>results</code> directory provides the scalability graphs for all our executed experiments.</p> <p><strong>Visualization of the Scalability Graph</strong></p> <p>The <code>scalability-graph-plotter.ipynb</code> notebook creates PDF plots of a scalability graph and allows to combine multiple scalability graphs in one plot. It can be adjusted to match the desired visualization.</p> <p><strong>Acknowledgments</strong></p> <p>This research is funded by the German Federal Ministry of Education and Research (BMBF) under grant no. 01IS17084 and is part of the <a href="https://www.industrial-devops.org">Titan project</a>.</p>
Master Thesis Replication Package for: Empirical Scalability Evaluation of Hopping Window Aggregation Methods in Distributed Stream Processing
<p>Master Thesis Replication Package for: Empirical Scalability Evaluation of Hopping Window Aggregation Methods in Distributed Stream Processing</p> <p>A detailed description can be found in the <em>README.md</em>.</p>
Data for 'Using 40 years of spot measurements to assess stream temperature response and recovery for different harvesting systems in northern hardwood forests' by Jason Leach, Danielle Hudson and R. Dan Moore. Submitted to Hydrological Processes.
<p>This dataset contains spot stream temperature measurements taken at 5 headwater streams draining forested hillslopes (C31, C32, C33, C34, C35) in the Turkey Lakes Watershed, approximately 65 km northwest of Sault Ste. Marie, Ontario, Canada.</p>
Historical legacies and contemporary processes shape beta diversity in Neotropical montane streams
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Stochastic processes and ecological connectivity drive stream invertebrate community responses to short-term drought
1. Community responses to and recovery from disturbances depend on local (e.g. presence of refuges) and regional (connectivity to recolonization sources) factors. Droughts are becoming more frequent in boreal regions, and are likely to constitute a severe disturbance for boreal stream communities where organisms largely lack adaptations to such hydrological extremes. 2. We conducted an experiment in 24 seminatural stream flumes to assess the effects of local and regional factors on the responses of benthic invertebrate communities to a short-term drought. We manipulated flow (drought vs. constant-flow), spatial arrangement of leaf litter patches (aggregated vs. evenly distributed) and colonization from regional species pool (enhanced vs. ambient connectivity) to test the combined effects of disturbance, connectivity, and resource arrangement on the structural and functional responses of benthic invertebrate communities. 3. We found that a drought as short as one week reduced invertebrate richness and abundances, mainly through stochastic extinctions. Such changes in richness were not reflected in functional diversity. This suggests that communities were characterized by a high degree of functional redundancy, which allowed maintenance of functional diversity despite species losses. Feeding groups responded differently to drought, with organic matter decomposers responding more than scrapers and predators. 4. Three weeks was insufficient for a complete invertebrate community recovery from the drought. However, recovery was greater in channels subjected to enhanced connectivity, which increased taxonomic diversity and abundance of certain taxa. Spatial configuration of resources explained the least variation in our response variables, having a significant effect only on invertebrate abundance and evenness (both sampling occasions) and species richness (end of recovery period). 5. Even a short drought, if occurring late to the season, may not allow communities to recover before the onset of winter, thus having a potentially long-lasting effect on stream communities. For boreal headwaters, extreme dewatering poses a novel disturbance regime that may trigger substantial and potentially irreversible changes. An improved understanding of such changes is needed to underpin adaptive management strategies in these increasingly fragmented and disturbed ecosystems. 18-Dec-2020
Data from: Environmental degradation results in contrasting changes in the assembly processes of stream bacterial and fungal communities
Environmental degradation may have strong effects on community assembly processes. We examined the assembly of bacterial and fungal communities in anthropogenically altered and near-pristine streams. Using pyrosequencing of bacterial and fungal DNA from decomposed alder Alnus incana leaves, we specifically examined if environmental degradation deterministically decreases or increases the compositional turnover of bacterial and fungal communities. Our results showed that near-pristine streams and anthropogenically altered streams supported distinct fungal and bacterial communities. The mechanisms assembling these communities were different in near-pristine and altered environments. Environmental disturbance homogenized bacterial communities, whereas fungal communities were more dissimilar in disturbed sites than in near-pristine sites. Compositional variation of both bacteria and fungi was related to water chemistry variables in disturbed sites, further implying the influence of environmental degradation on community assembly. Bacterial and fungal communities in near-pristine streams were weakly controlled by environmental factors, suggesting that the relative importance of niche-based versus neutral processes in assembling microbial communities may strongly depend on the spatial scale and local environmental context. Our results thus suggest that environmental degradation may strongly affect the composition and β-diversity of stream microbial communities colonizing leaf litter, and that the direction of the change can be different between bacteria and fungi. A better understanding of the environmental tolerances of microbes and the mechanisms assembling microbial communities in natural environmental settings is needed to predict how environmental alteration is likely to affect microbial communities.
Simulation output of the reference setup in "The comparative role of physical system processes in Hudson Strait ice stream cycling: a comprehensive model-based test of Heinrich event hypotheses"
<p>This supplementary material for "The comparative role of physical system processes in Hudson Strait ice stream cycling: a comprehensive model-based test of Heinrich event hypotheses" contains the simulation output of the 20 reference runs. Additional data is available upon request from the corresponding author.</p>
Data from: Parallel cognitive processing streams in human prefrontal cortex: parsing areal-level brain network for response inhibition
<p>Multiple cognitive processes are recruited to achieve adaptive behavior. However, it is poorly understood how such cognitive processes are implemented in temporal cascades of human cerebral cortical areas as processing streams to achieve behavior. In the present study, we identify cortical processing streams for response inhibition and examine relationships among the processing streams. Functional magnetic resonance imaging (MRI) and time-resolved single-pulse transcranial magnetic stimulation (TMS) reveal three distinct critical timings of transient disruption in the functionally essential cortical areas that belong to two distinct cerebrocortical networks. Furthermore, single-pulse TMS following suppression of the ventral posterior inferior frontal cortex (vpIFC) with repetitive TMS reveals information flow from the vpIFC to the presupplementary motor area (preSMA) within the same network but not to the dorsal posterior inferior frontal cortex (dpIFC) across different networks. These causal behavioral effects suggest two parallel processing streams (vpIFC-preSMA versus dpIFC-intraparietal sulcus) that act concurrently during response inhibition.</p>
Data from: Parallel cognitive processing streams in human prefrontal cortex: parsing areal-level brain network for response inhibition
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Data from: Environmental degradation results in contrasting changes in the assembly processes of stream bacterial and fungal communities
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Stochastic processes and ecological connectivity drive stream invertebrate community responses to short-term drought
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The Spleen as a Target to Characterize Immunomodulatory Effects of Down-Stream Processed Cyberlindnera jadinii Yeasts in Atlantic Salmon Exposed to a Dietary Soybean Meal Challenge
GEO Series GSE174262. Salmo salar. 16 samples. Type: Expression profiling by high throughput sequencing.
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.