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2,235 results for “engineering”
Engineering Archive's first 1000 posted eprints
<p>The file 'EngineeringArchive_first1000eprints.zip' contains PDFs of the first 1000 eprints (files named using each submission's GUID) submitted to and hosted on Engineering Archive, the eprint server for engineering. The file 'engrxiv-papers.csv' is a listing of the metadata for these 1000 eprints. This represents a snapshot in time of the status of Engineering Archive (engrXiv) on 2020-05-26.</p> <p>The eprint PDFs are the copyrighted works of their respective authors and are licensed as indicated on the associated page on engrxiv.org.</p>
Data for marine fungi engineered to convert D-galacturonic acid to galactaric acid
<p>Two Excel files are stored, related to the successful engineering of two marine fungi (<em>Trichoderma</em> sp. LF328 and <em>Coniochaeta sp</em>. MF729) for the production of galactaric (mucic) acid from D-galacturonic acid, which is described in Vidgren et al. 2020. One file contains the HPLC data for cultivations carried out in 24-well plates with medium containing D-galacturonic acid, poly-D-galacturonic acid or pectin as a source of D-galacturonic acid, with lactose, xylose, glucose or glycerol as co-substrates. Cultures were sampled on days 3, 4 and 6, and sometimes on days 5 and 7, as indicated in the data. Data for negative results (e.g. lack of consumption of galactaric acid by the parent strains) is also included.</p> <p>The second file contains the HPLC data for cultivations carried out in 1 (<em>Coniochaeta sp</em>. C1) or 1.5 L (<em>Trichoderma</em> sp. T2) bioreactors, with information on culture volume and product yield for <em>Trichoderma</em> sp. T2.</p>
Data from: Migrating bison engineer the green wave
<p>Newly emerging plants provide the best forage for herbivores. To exploit this fleeting resource, migrating herbivores align their movements to surf the wave of spring green-up. With new technology to track migrating animals, the Green Wave Hypothesis has steadily gained empirical support across a diversity of migratory taxa. This hypothesis assumes the green wave is controlled by variation in climate, weather, and topography, and its progression dictates the timing, pace, and extent of migrations. However, aggregate grazers that are also capable of engineering grassland ecosystems make some of the world's most impressive migrations, and it is unclear how the green wave determines their movements. Here we show that Yellowstone's bison (Bison bison) do not choreograph their migratory movements to the wave of spring green-up. Instead, bison modify the green wave as they migrate and graze. While most bison surfed during early spring, they eventually slowed and let the green wave pass them by. However, small-scale experiments indicated that feedback from grazing sustained forage quality. Most importantly, a 6-fold decadal shift in bison density revealed that intense grazing caused grasslands to green up faster, more intensely, and for a longer duration. Our finding broadens our understanding of the ways in which animal movements underpin the foraging benefit of migration. The widely accepted Green Wave Hypothesis needs to be revised to include large aggregate grazers that not only move to find forage, but also engineer plant phenology through grazing, thereby shaping their own migratory movements.</p>
Augmented emission maps: 1598 cc 85 kW Euro 6 diesel engine: update 1
<p>In order to enable the sharing of data the emission data for vehicles is standardized. The data exchange format contains all data that is applicable for a specific engine taxonomy code.</p> <p>This specific data set refers to the 1598 cc 85 kW Euro 6 diesel engine that has been applied in several Volkswagen group models (Volkswagen Golf, Golf Sportsvan, Touran and T-roc, Audi A1, A1 Sportback, A3, A3 Limousine, A3 Sportback and Q2, Seat Ibiza, Arona, Leon, Ateca and Toledo, Skoda Rapid, Octavia, Karoq). Note that the Hyundai/Kia engine with the same specifications (but a different alliance code) is not covered.</p> <p>The standardized emission map has a “.map.txt” extension and is also human readable. The files starts with metadata which contains information about:</p> <ul> <li>the engine taxonomy code,</li> <li>total driven kilometers over which the data was gathered,</li> <li>total time in hours over which the data was gathered,</li> <li>the number of vehicles which were tested to create the emission map,</li> <li>the DOI (Digital Object Identifier) reference,</li> <li>Which emission maps are available in the file.</li> </ul> <p>The DOI 10.5281/zenodo refers to a meta-data document that provides the full description of the standardized emission map.</p>
Augmented emission maps: 1199 cc 55 kW Euro 4 petrol engine: update 1
<p>In order to enable the sharing of data the emission data for vehicles is standardized. The data exchange format contains all data that is applicable for a specific engine taxonomy code.</p> <p>This specific data set refers to the 1199 cc 55 kW Euro 4 petrol engine that has been applied in the Opel, Agile and Astra.</p> <p>The standardized emission map has a “.map.txt” extension and is also human readable. The files starts with metadata which contains information about:</p> <ul> <li>the engine taxonomy code,</li> <li>total driven kilometers over which the data was gathered,</li> <li>total time in hours over which the data was gathered,</li> <li>the number of vehicles which were tested to create the emission map,</li> <li>the DOI (Digital Object Identifier) reference,</li> <li>Which emission maps are available in the file.</li> </ul> <p>The DOI 10.5281/zenodo refers to a meta-data document that provides the full description of the standardized emission map.</p>
Scripts from: Controlling evolution in genetically engineered systems through repeated introduction
<p>Genetically engineered transgenes evolve in the same way as other genetic material. However, evolution in engineered genes is often undesirable and can negatively affect their stability, frequency, and efficacy over time. Methods that maintain the stability of engineered genomes are therefore critical to the successful design and use of genetically engineered organisms. One potential method to limit unwanted evolution is by taking advantage of the ability of gene-flow to counter local adaption, a process of supplementation. Here we investigate the feasibility of supplementation as a mechanism to offset the evolutionary degradation of a transgene in three model systems: a bioreactor, a gene drive, and a transmissible vaccine. In each model, continual introduction from a stock is used to balance mutation and selection against the transgene. Each system has its unique features. The bioreactor system is especially tractable and has a simple answer: the level of supplementation required to maintain the transgene at a frequency is approximately , where <i>s</i> is the selective disadvantage of the transgene. Supplementation is also feasible in the transmissible vaccine case but is probably not practical to prevent the evolution of resistance against a gene drive. We note, however, that the continual replacement of even a small fraction of a large population can be challenging, limiting the usefulness of supplementation as a means of controlling unwanted evolution.</p>
Neural Reverse Engineering of Stripped Binaries using Augmented Control Flow Graphs
<p>This dataset and pre-trained models are released as a companion to our OOPSLA '20 publication: "Neural Reverse Engineering of Stripped Binaries using Augmented Control Flow Graphs":</p> <ol> <li>The dataset file (<a href="https://zenodo.org/api/files/0e98c909-50b4-43e3-8ab2-bb9673eed786/nero_dataset_binaries.tar.gz?versionId=b943752a-e89e-43c1-b32f-19d4a7420e00">nero_dataset_binaries.tar.gz</a>) is composed from packages of binary executables created by compiling several GNU source-code packages. We used these executables to evaluate our approach as implemented in our prototype "Nero" and compare it to other approaches. All executables contain debug information which serves as the ground truth for the procedure name predictions. The packages are split into three sets: training, validation and test. <ol> <li>The executable file name structure is: "<compiler>-<compiler version>__O<Optimization level(u for default)>__<Package name>[-<optional package version>]__<Executable name>". For example "gcc-5__Ou__cssc__sccs".</li> </ol> </li> <li>The procedure representation file (<a href="https://zenodo.org/api/files/0e98c909-50b4-43e3-8ab2-bb9673eed786/procedure_representations.tar.gz?versionId=fef2380c-c869-4a3f-8e32-bc4972dbb219">procedure_representations.tar.gz</a>) contains: <ol> <li>The raw representations for all the binary procedures in the above dataset. Each procedure is represented by one line in the relevant file for each set (training.json, validation.json and test.json) </li> <li>The above representations preprocessed for training.</li> </ol> </li> <li>The pre-trained model file (<a href="https://zenodo.org/api/files/0e98c909-50b4-43e3-8ab2-bb9673eed786/nero_gnn_model.tar.gz">nero_gnn_model.tar.gz</a>) was created using the above preprocessed dataset and contains: <ol> <li>Pre-trained model.</li> <li>Training log.</li> <li>Prediction results log.</li> </ol> </li> </ol> <p>For the code of the "Nero" prototype, and more information about the above artifacts see <a href="https://github.com/tech-srl/Nero">our Github repo</a></p>
Augmented emission maps: 1968 cc 55 Kw Euro 6 diesel engine: update 2
<p>In order to enable the sharing of data the emission data for vehicles is standardized. The data exchange format contains all data that is applicable for a specific engine taxonomy code.</p> <p>This specific data set refers to the 1968 cc 55 kW Euro 6 diesel engine that has been applied in the Volkswagen Caddy.</p> <p>The standardized emission map has a “.map.txt” extension and is also human readable. The files starts with metadata which contains information about:</p> <ul> <li>the engine taxonomy code,</li> <li>total driven kilometers over which the data was gathered,</li> <li>total time in hours over which the data was gathered,</li> <li>the number of vehicles which were tested to create the emission map,</li> <li>the DOI (Digital Object Identifier) reference,</li> <li>Which emission maps are available in the file.</li> </ul> <p>The DOI <a href="http://doi.org/10.5281/zenodo.4268034">http://doi.org/10.5281/zenodo.4268034</a> refers to a updated meta-data document that provides the full description of the standardized emission map.</p>
Augmented emission maps: 1199 cc 55 kW Euro 5a diesel engine: update 2
<p>In order to enable the sharing of data the emission data for vehicles is standardized. The data exchange format contains all data that is applicable for a specific engine taxonomy code.</p> <p>This specific data set refers to the 1199 cc 55 kW Euro 5a diesel engine that has been applied in the Volkswagen Polo, Seat Ibiza, Skoda Fabia and Skoda Roomster.</p> <p>The standardized emission map has a “.map.txt” extension and is also human readable. The files starts with metadata which contains information about:</p> <ul> <li>the engine taxonomy code,</li> <li>total driven kilometers over which the data was gathered,</li> <li>total time in hours over which the data was gathered,</li> <li>the number of vehicles which were tested to create the emission map,</li> <li>the DOI (Digital Object Identifier) reference,</li> <li>Which emission maps are available in the file.</li> </ul> <p>The DOI <a href="http://doi.org/10.5281/zenodo.4268034">http://doi.org/10.5281/zenodo.4268034</a> refers to a updated meta-data document that provides the full description of the standardized emission map.</p>
Data for 'Ultrafast strain engineering and coherent structural dynamics from resonantly driven optical phonons in LaAlO3'
<p>This folder contains all the raw data required to generate the figures in the work 'Ultrafast strain engineering and coherent structural dynamics from resonantly driven optical phonons in LaAlO3'. </p> <p> </p> <p><strong>Contents</strong></p> <p><em>Figure 2:</em></p> <p>(a) Time resolved balanced reflection data for the short and long time periods: LongTimeData.txt and ShortTimeData.txt. The corresponding Fourier spectra shown in the inset: InsetFourier_LongTimeData.txt and InsetFourier_ShortTimeData.txt</p> <p>(b) Time resolved polarization rotation data for the short and long time periods: LongTimeData.txt and ShortTimeData.txt. The corresponding Fourier spectra shown in the inset: InsetFourier_LongTimeData.txt and InsetFourier_ShortTimeData.txt</p> <p><em>Figure 3:</em></p> <p>(a) Time-resolved polarization rotation data after excitation at two different pump photon energies: trace_85meV_excitation.txt and trace_124meV_excitation.txt. Amplitude as function of central pump photon energy: Inset_Wavelength_dependence.txt and the absorption of LaAlO3: Inset_absorption.txt</p> <p>(b) The raw data for different polarizations: traces_angle_dependence.txt</p> <p>(c) The experimentally obtained (AmplitudeExperiment.txt) and DFT calculated values (AmplitudeDFT.txt).</p> <p><em>Figure 4</em></p> <p>(a) Fourier spectra (and double Gaussian fit) corresponding to time-resolved measurments after excitation at different central photon energies: FourierSpectraData.txt (and FourierSpectraFits). Inset: Extracted peak amplitude (Inset_StrainvsWavelength.txt) and Gaussian fit of the TA strain (Inset_GaussianFit.txt). </p> <p>(b)The TA/LA ratio as function of pump photon energy (SoundWaveRatio.txt) and the Lorentzian fit (RatioFit.txt). And the absorption of LaAlO3 (Absorption_coefficient.txt).</p> <p> </p> <p> </p> <p> </p> <p> </p>
Mechanical Engineering: An International Journal ( MEIJ)
<pre>Mechanical Engineering: An International Journal is a peer-reviewed, open access journal that addresses the impacts and challenges of Mechanical Engineering. The journal documents practical and theoretical results which make a fundamental contribution for the development of Mechanical Engineering.</pre>
Data of publication: Defect engineering for Quantum Grade Rare-Earth Nanocrystals
<p>Data corresponding to the figures of the publication "Defect Engineering for Quantum Grade Rare-Earth Nanocrystals" by S. Liu et al. (hhttps://pubs.acs.org/doi/10.1021/acsnano.0c02971). A text file describes data in each compressed folder, please refer to the publication for more details. </p>
An exploration of the codes of ethics of various organisations that hire software engineers.
<p>A spreadsheet identifying ethical imperatives discovered in the published codes of practice from U.S. technology firms and U.K. universities.</p>
Mechanical Engineering: An International Journal (MEIJ)
<p>CALL FOR PAPERS...!!!!</p> <p> </p> <p><strong>Mechanical Engineering: An International Journal (MEIJ)</strong></p> <p> </p> <p><a href="http://airccse.com/meij/index.html"><strong>http://airccse.com/meij/index.html</strong></a></p> <p> </p> <p><strong>ISSN: 2349 – 2651</strong></p> <p> </p> <p><strong>Submission Deadline:</strong> <strong>January 30</strong> <strong>, 2021</strong></p> <p> </p> <p><strong>CONTACT US : </strong><a href="mailto:meijjournal@yahoo.com"><strong>meijjournal@yahoo.com</strong></a><strong> or </strong><a href="mailto:meij@airccse.com"><strong>meij@airccse.com</strong></a></p> <p> </p> <p><strong>Submission Link</strong> : <a href="https://airccse.com/submission/home.html"><strong>https://airccse.com/submission/home.html</strong></a></p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
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>
Survey of software engineering in code used in published papers
<p><strong>Background</strong>: Computer code underpins modern science, and at the present time has a crucial role in leading our response to the COVID-19 pandemic. While models are routinely criticised for their assumptions, the algorithms and the quality of code implementing them often avoid scrutiny and, hence, scientific conclusions cannot be rigorously justified.</p> <p><strong>Problem</strong>: Assumptions in programs are hard to scrutinise as they are rarely explicit in published work. In addition, both algorithms and code have bugs, effectively unknown assumptions that have unwanted effects.</p> <p>Code is fallible. Any model interpretation that relies on code is therefore fallible, and if the code is not published with adequate documentation, the code cannot be scrutinised. In turn, the scientific claims cannot be properly scrutinised.</p> <p><strong>Solutions</strong>: Code can be made much more reliable using software engineering good practice. Three specific solutions are proposed. First, professional software engineers can help and should be involved in critical research. Secondly, "Software Engineering Boards" (supplementing and analogous to Ethics or Institutional Review Boards) must be instigated and used. Thirdly, code, when used, must be considered an intrinsic part of any publication, and therefore must be formally reviewed by competent software engineers.</p> <p><em>The paper's Supplementary Material includes a summary of professional software engineering best practice, particularly as applied to scientific research and publication.</em></p>
Consumer movement dynamics as hidden drivers of stream habitat structure: suckers as ecosystem engineers on the night shift
Ecosystem engineers engineering can control the spatial and temporal distribution of resources and movement by engineering organisms within an ecosystem can transport mobilize resources across boundaries and distribute engineering effects. Movement patterns of fishes can cause physical changes to stream aquatic habitats though nesting or feeding, both of which often vary in space and time. Here we present evidence of ecosystem engineering by the Sonora sucker (Catostomus insignis), a dominant fish in streams of the southwestern United States, and show how cryptic nocturnal movement patterns and bioturbation activities control heterogeneity in benthic substrates, and in sediment and carbon export. Sonora suckers exhibit distinct diel movement patterns, spending daylight hours in refuge habitats (typically deep pools) while moving into shallow habitats at night to feed. Feeding by suckers creates substantial disturbance in soft sediments that are patchy in space and time. These disturbances moved up to 2.4 × 104 cm3 of sediment per square meter per week in locations that are up to hundreds of meters away from sucker daytime refuges. The diel cycles in feeding activity (i.e., nocturnal digging in benthic substrates) caused nighttime pulses in suspended sediment that comprised up to 32% of the daily suspended load and organic matter transport of a stream reach. During the daytime, this particulate transport settles in habitats beyond the location of the initial disturbance, thus redistributing both sediment and organic matter. Our data indicate that cryptic movement by ecosystem engineers can distribute their effects in space and time generating heterogeneity in resources and suggest that habitat modifications restricting consumer movement may alter the impact of engineering activities.
Data from: Environmental gradients determine the potential for ecosystem engineering effects
Understanding processes that determine biodiversity is a fundamental challenge in ecology. At the landscape scale, physical alteration of ecosystems by organisms, called ecosystem engineering, enhances biodiversity worldwide by increasing heterogeneity in resource conditions and enhancing species coexistence across engineered and non-engineered habitats. Engineering-diversity relationships can vary along environmental gradients due to changes in the amount of physical structuring created by ecosystem engineering, but it is unclear how this variation is influenced by the responsiveness of non-structural abiotic properties to engineering. Here we show that environmental gradients determine the capacity for engineering to alter resource availability and species diversity, independent of the magnitude of structural change produced by engineering. We created an experimental rainfall gradient in an arid grassland where rodents restructure soils by constructing large, long-lasting burrows. We found that greater rainfall increased water availability and productivity in both burrow and inter-burrow habitats, causing a decline in local (alpha) plant diversity within both of these habitats. However, increased rainfall also resulted in greater differences in soil resources between burrow and inter-burrow habitats, which increased species turnover (beta diversity) across habitats and stabilized landscape-level (gamma) diversity. These responses occurred regardless of rodent presence and without changes in the extent of physical alteration of soils by rodents. Our results suggest that environmental gradients can influence the effects of ecosystem engineering in maintaining biodiversity via resource heterogeneity and species turnover. In an era of rapid environmental change, accounting for this interaction may be critical to conservation and management.
Parasitism in ecosystem engineer species: a key factor controlling marine ecosystem functioning
<p>1. Although parasites represent a substantial part of marine communities' biomass and diversity, their influence on ecosystem functioning, especially via the modification of host behaviour, remains largely unknown. Here, we explored the effects of the bopyrid ectoparasite Gyge branchialis on the engineering activities of the thalassinid crustacean Upogebia pusilla and the cascading effects on intertidal ecosystem processes (e.g. sediment bioturbation) and functions (e.g. nutrient regeneration).</p> <p>2. Laboratory experiments revealed that the overall activity level of parasitized mud shrimp is reduced by a factor 3.3 due to a decrease in time allocated to burrowing and ventilating activities (by a factor 1.9 and 2.9 respectively).</p> <p>3. Decrease in activity level led to strong reductions of bioturbation rates and biogeochemical fluxes at the sediment-water interface.</p> <p>4. Given the worldwide distribution of mud shrimp and their key role in biogeochemical processes, parasite-mediated alteration of their engineering behaviour has undoubtedly broad ecological impacts on marine coastal systems functioning.</p> <p>5. Our results illustrate further the need to consider host-parasite interactions (including trait-mediated indirect effects) when assessing the contribution of species to ecosystem properties, functions and services.</p>
Data from: Macro-detritivores assist resolving the dryland decomposition conundrum by engineering an underworld heaven for decomposers
<p>Litter decomposition in most terrestrial ecosystems is regulated by moisture-dependent microorganism activity, among other things. <span class="fontstyle01"><span>Decomposition models typically underestimate rates of plant litter decomposition in drylands, suggesting the existence of additional drivers of decomposition. Attempts to reveal these drivers have predominantly focused on abiotic degradation agents, alternative moisture sources,</span></span> and <span class="fontstyle01"><span>soil-litter mixing</span></span>. The role of burrowing animals in promoting decomposition has received less attention despite greatly contributing to plant litter transfer from the harsh desert surface to the moister and nutrient-rich environment belowground. Our goal was to explore how macro-detritivore burrows affect plant litter mineralization dynamics. We introduced <sup>13</sup>C-labeled litter belowground into (1) desert isopod (<i>Hemilepistus reaumuri</i>) burrows and (2) artificial burrows, and aboveground on top of (3) isopod fecal pellet mounds and (4) bare soil crust. We compared the litter mass loss between the four treatments and used cavity ring-down spectroscopy to reveal the <i>in situ</i> mineralization dynamics. No litter mineralization was evident during the dry summer months both above- and belowground. Following rain events, mineralization rates spiked in all four micro-environments, quickly diminishing aboveground while slowly waning belowground. Total litter mass loss was twofold higher below- than aboveground and was significantly higher in isopod burrows compared to artificial burrows. Our findings demonstrate that burrowing macro-detritivores promote litter decomposition in deserts by transferring organic matter to their burrows where favorable climatic conditions and a nutrient-enriched environment foster microbial activity. Thus, attempts to resolve the dryland decomposition conundrum should not be limited to exploring factors that allow decomposition under harsh desert surface climatic conditions, but focus on the role that animals play in facilitating decomposer-friendly environments to which they translocate plant litter.</p>
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.