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69 results for “Network experimentation”
Air and soil temperature data from the Reference Stand network at the Andrews Experimental Forest, 1971 to present
The current network of temperature measurement sites are designed to represent spatial variability of air and soil temperature in rugged mountain topography, and serve as second-level stations to capture specific microclimate temperatures in conjunction with a network of Benchmark Meteorological Stations (MS001). The air and soil thermograph network has been reduced from the historical network of 37 sites originally established. Currently there are 10 measurement sites with two of these sites measuring relative humidity in addition to air and soil temperature. An original network of 19 sites (RS01-RS19) were established during the International Biome Program in the early 1970's. Emphasis on phenology, plant moisture stress, and leaf nutrient content led to extending this network of air and soil temperature measurement. A plant community classification system (Dyrness et al., 1971) was used as a primary means of stratification, and a set of permanent vegetation plots (Reference Stands) was installed to represent forest communities with distinct vegetation and hypothesized different environments (Dyrness et al., 1974). A thermograph network was installed within the reference stands in the early 1970's (Zobel et al., 1974), and vegetation standing crop, tree growth and mortality, and plant succession were also measured. The majority of these sites were established to monitor micro-meteorological data under the canopy. The purpose of this network was to provide air and soil temperature data for modeling photosynthesis, respiration, phenology, and decomposition, and to measure environmental gradients.
Transportation network system including trails, road construction history, and gates for the Andrews Experimental Forest, 1952-2011
Transportation network locations within the Andrews Experimental Forest. Includes locations of all the roads, trails, and gates within and around the forest. Original road layer was drawn on maps in 1992 and field validated. The road construction history (1952-1990) has been captured as an attribute. Roads were updated in 2004 to include roads that have been abandoned. Gates were field checked in 2004, as well as trail locations. The three data sets were updated after the 2008 LiDAR data was delivered. Roads were digitized on-screen from the bare-earth DEM, and gates were moved to match the new road network. Trails were updated for the 2011 Andrews map update. Many were located through GPS, and new trails were added. The original data is represented, as well as the updated datasets. The road network dataset is in an esri file geodatabase format, and the other datasets are in esri shapefile format, and all are in a zipped file format.
Stream and air temperature data from stream network in the Andrews Experimental Forest, 1997-2001
This study examines stream temperatures and associated air temperatures at multiple sites in stream networks within the Andrews Experimental Forest. Stream temperature sensors were placed at matched elevations in the main headwater streams of Lookout Creek, Mack Creek and McRae Creek as well as above and below major confluences in downstream reaches. Air temperatures were recorded 1.5 m above the stream at selected sites. Data were collected every half hour during late spring and summers. Some sites have data during fall and winter. Sensors were also placed in bottom of shallow piezometric wells in WS 3.
Advanced Resolution Canopy FLOw (ARCFLO) experiment employing the SUbcanopy Sonic Anemometer Network (SUSAN) in WS01 of the HJ Andrews Experimental Forest, July-September 2012
This dataset was collected during one of the ARCFLO (Advanced Resolution Canopy FLOw) experiment series’ field campaigns. This field campaign was carried out in WS1 of the HJ Andrews Experimental forest during July-September 2012 by the biomicrometeorology group, PI Christoph Thomas. The ARCFLO experiment series spanned a wide range of topographic conditions (flat, sloped, mountainous) and canopy architectures (grassland, orchard, open forest, dense forest) and was carried out between 2011 and 2014. It was funded through the NSF Career Award in Physical and Dynamical Meteorology to PI Christoph Thomas. The main goal of this project was to develop a novel improved framework to describe the airflow and its transport under weak-wind conditions for a continuous variation of overstory density and stratification. The objective is to i) identify forcing mechanisms of submeso motions, ii) evaluate the impact of plant canopies of different overstory density on the wind, temperature, and humidity fields, and iii) improve predictors for mixing in plant canopies that incorporate the important physical mechanisms. Observations were be made with a unique combination of new and standard techniques including optical fiber measurement of temperature structure, acoustic remote sensing, ultrasonic anemometers, and laser-illuminated flow visualizations.
Monthly precipitation data from a network of standard gauges at the Jornada Experimental Range (Jornada Basin LTER) in southern New Mexico, January 1916 - ongoing
This ongoing dataset contains monthly precipitation measurements from a network of standard can rain gauges at the Jornada Experimental Range in Dona Ana County, New Mexico, USA. Precipitation physically collects within gauges during the month and is manually measured with a graduated cylinder at the end of each month. This network is maintained by USDA Agricultural Research Service personnel. This dataset includes 39 different locations but only 29 of them are current. Other precipitation data exist for this area, including event-based tipping bucket data with timestamps, but do not go as far back in time as this dataset.
SIMBED - Offline Real-World Wireless Networking Experimentation using ns-3
<p>R&D in wireless networking typically depends on experimentation to make realistic evaluations, since simulation is inherently a simplification of the real-world. However, experimentation is limited in aspects where simulation excels, such as repeatability and reproducibility.</p> <p>Real wireless experiments are hardly repeatable. Given the same input they can produce very different output results, since wireless communications are influenced by external random phenomena such as noise, interference, and multipath. Real experiments are also difficult to reproduce: either the original community testbed is unavailable – offline or running other experiments – or the custom testbed used is inaccessible.</p> <p>Fed4FIRE+ wireless testbeds such as w-iLab.t and NITOS, although deployed in controlled environments, do not fully address the problem. The CONCRETE tool used in such testbeds assures the repeatability and reproducibility of experiments, but ignores executions whose results are also representative of the system operation and often reveal unpredicted behaviour that must be understood.</p> <p>What if we could make any wireless experiment repeatable and reproducible under the same exact conditions? What if we could share the same Fed4FIRE+ testbed execution conditions among an "infinite" number of users? What if we could run wireless experiments faster than in real time?</p> <p>INESC TEC has been developing the Offline Experimentation (OE) approach that combines the best of simulation and experimentation to achieve the above-mentioned goals. By relying on Network Simulator 3 (ns-3) and its good simulation capabilities from the MAC to the application layer, we have been exploring how ns-3 can be used to replicate real-world wireless experiments using real traces containing 1) position of nodes and 2) the quality of each radio link.</p> <p>The <strong>SIMBED </strong>project aimed at running a set of wireless experiments on top of the controlled environments of w-ilab.t and NITOS Fed4FIRE+ testbeds to further validate the OE approach. For that purpose, we configured different fixed and mobile experimental scenarios, representative of Wi-Fi range of operation, and measured the attained network performance using metrics such as throughput and Round-Trip Time (RTT). Then, we repeated each experiment using, both, Pure Simulation (PS) and OE approaches based on ns-3, also measuring the network performance for the same set of executions of experiments for all the different scenarios.</p> <p>By comparing the performance metrics of each real experiment with its PS and OE counterparts, we were able to measure the relative error of each simulation approach relatively to the real experiments, as well as the accuracy gains introduced by the OE approach when compared to the PS traditional alternative. The main results show that it is possible to repeat and reproduce real experiments in ns-3, using the OE approach, achieving closer to real performance than using the PS approach. For all the experiments performed in SIMBED, using the OE approach resulted in an average accuracy gain of 59% when comparing to the PS approach. </p> <p>These results were important for validating a PhD thesis contribution related to the OE approach, as well as for producing two conference papers and one journal paper. The SIMBED results increased our confidence on the accuracy of the OE approach and are envisioned to foster the adoption of the OE approach by the networking community, in complement to the use of real experimentation.</p> <p> </p> <p>The following dataset presents the results of the SIMBED project, organized in different folders, for each subset of experiments carried on:</p> <ul> <li><strong><em>SubExp#1: </em></strong><em>Static point-to-point Wi-Fi communications using auto-rate (Minstrel) </em> <ul> <li><strong><em>SubExp#1.1:</em></strong><em> Using w-iLab.2 (medium to high SNR scenarios)</em></li> <li><strong><em>SubExp#1.2:</em></strong><em> Using w-iLab.2 (low SNR scenarios)</em></li> <li><strong><em>SubExp#1.3:</em></strong><em> Using NITOS</em></li> <li><strong><em>SubExp#1.4:</em></strong><em> Using w-iLab.1 (datacenter room)</em></li> </ul> </li> <li><strong><em>SubExp#2: </em></strong><em>Static point-to-point Wi-Fi communications using fixed</em> rate</li> <li><strong><em>SubExp#3: </em></strong><em>Mobile point-to-point Wi-Fi communications using auto-rate (Minstrel)</em></li> <li><strong><em>SubExp#4: </em></strong><em>Static multiple access Wi-Fi communications using auto-rate (Minstrel) </em> <ul> <li><strong><em>SubExp#4.1:</em></strong><em> Using w-iLab.2 (bidirectional) (medium to high SNR scenarios)</em></li> <li><strong><em>SubExp#4.2:</em></strong><em> Using w-iLab.2 (bidirectional) (low SNR scenarios)</em></li> <li><strong><em>SubExp#4.3:</em></strong><em> Using NITOS (bidirectional)</em></li> <li><strong><em>SubExp#4.4:</em></strong><em> Using w-iLab.1 (bidirectional)</em></li> <li><strong><em>SubExp#4.5:</em></strong><em> Using NITOS (2 STAs)</em></li> <li><strong><em>SubExp#4.6:</em></strong><em> Using w-iLab.2 (2 STAs)</em></li> </ul> </li> <li><strong><em>SubExpExample</em></strong>: contains raw experimental logs, parsed data and simulation results, to show how data extracted from the nodes is processed to be compatible with the OE approach and comparable with OE and PS simulation results.</li> </ul> <p>Each experiment has an individual folder, named according to the date and time of the experiment and the nodes used. Inside, there’s a folder for the <strong>parsed</strong> experimental results, which contains</p> <p>This folder contains the details and parsed logs of the experiment, as follows:</p> <ul> <li><em>date_time</em><strong>.cfg </strong>– configuration details of the experiment</li> <li><em>date_time_NodeID<sup><a href="#_ftn1"><strong>[1]</strong></a></sup>_SenderID<sup><a href="#_ftn2"><strong>[2]</strong></a></sup>_ReceiverID<sup><a href="#_ftn3"><strong>[3]</strong></a></sup>_FlowType<sup><a href="#_ftn4"><strong>[4]</strong></a></sup>_Params<sup><a href="#_ftn5"><strong>[5]</strong></a></sup></em><strong>.snr </strong>– logs of the Signal/Noise ratio (1 file per node/flow) </li> <li><em>date_time_NodeID_SenderID_ReceiverID_FlowType_Params</em><strong>.stats</strong> – logs of the packets received (1 file per node/flow) </li> <li><em>NodeID</em><strong>.</strong><strong>waypoints</strong> – coordinates of the static nodes</li> <li><em>date_time_MobileNodeID</em><strong>.</strong><strong>waypoints</strong> – waypoints of the mobile nodes (when applicable)</li> </ul> <p>The experiment’s folder also contains a folder for the simulations <strong>output</strong> with the simulations statistics files, for the multiple simulations approaches considered, as follows:</p> <ul> <li><em>date_time_NodeID_SenderID_ReceiverID_FlowType_Params</em>.<strong>simstats </strong>– logs of the packets received (simulation)</li> </ul> <p> </p> <p><sub><a href="#_ftnref1">[1]</a> ID of the node Logging node</sub></p> <p><sub><a href="#_ftnref2">[2]</a> ID of the Sender node</sub></p> <p><sub><a href="#_ftnref3">[3]</a> ID of the Receiver node</sub></p> <p><sub><a href="#_ftnref4">[4]</a> Flow type: Unidirectional, Bidirectional or Unidirectional with Multiple Access</sub></p> <p><sub><a href="#_ftnref5">[5]</a> Configurable parameters: Sender/Receiver Transmission Power and Data Rate (when applicable)</sub></p>
Experimental datasets of networks of nonlinear oscillators: Structure and dynamics during the path to synchronization
<p>The analysis of the interplay between structural and functional networks require experiments where both the specific structure of the connections between nodes and the time series of the underlying dynamical units are known at the same time. However, real datasets typically contain only one of the two ways (structural or functional) a network can be observed. Here, we provide experimental recordings of the dynamics of 28 nonlinear electronic circuits coupled in 20 different network configurations. For each network, we modify the coupling strength between circuits, going from an incoherent state of the system to a complete synchronization scenario. Time series containing 30000 points are recorded using a data-acquisition card capturing the analogic output of each circuit. The experiment is repeated three times for each network structure allowing to track the path to the synchronized state both at the level of the nodes (with its direct neighbors) and at the whole network. These datasets can be useful to test new metrics to evaluate the coordination between dynamical systems and to investigate to what extent the coupling strength is related to the correlation between functional and structural networks.</p> <p>We provide the times series of N=28 Rössler electronic oscillators for 20 different network configurations (compressed file with tag R1 to R20). For each network structure, we recorded the times series for 101 different coupling strengths between oscillators. Each one of the 101 corresponding files is labeled as ST_X_Y.dat where X is a value between X=0 and X=100 that corresponds, respectively, to the minimum and maximum coupling strength. The value of Y corresponds to the repetition number, which can be 1, 2 of 3 (i.e., we repeated the same experiment three times). Data files contain the second variable of the 28 nodes arranged in columns with a length of 30000 points. In a second file named Structure.zip, all the network structures are given, each file having a name Net_R.dat, where R=1, 2… 20. The degree of each node (i.e., number of output connections) is the same for all network configurations, where the specific neighbors of each node are re-arranged randomly.</p>
A "short blanket" dilemma for a state-of-the-art neural network potential for water: Reproducing experimental properties or the underlying many-body physics?
<p>Deep neural network (DNN) potentials have recently gained popularity in computer simulations of a wide range of molecular systems, from liquids to materials.<br> In this study, we explore the possibility of combining the computational efficiency of the DeePMD framework and the demonstrated accuracy of the MB-pol data-driven many-body potential to train a DNN potential for large-scale simulations of water across its phase diagram.<br> We find that the DNN potential is able to reliably reproduce the MB-pol results for liquid water but provides a less accurate description of the vapor-liquid equilibrium properties.<br> This shortcoming is traced back to the inability of the DNN potential to correctly represent many-body interactions.<br> An attempt to explicitly include information about many-body effects results in a new DNN potential that exhibits the opposite performance, being able to correctly reproduce the MB-pol vapor-liquid equilibrium properties but losing accuracy in the description of the liquid properties.<br> These results suggest that DeePMD-based DNN potentials are not able to correctly "learn" and, consequently, represent many-body interactions, which implies that DNN potentials may have limited ability to predict properties for state points that are not explicitly included in the training process.<br> The computational efficiency of the DeePMD framework can still be exploited to train DNN potentials on data-driven many-body potentials, which can thus enable large-scale, "chemically accurate" simulations of various molecular systems, with the caveat that the target state points must have been adequately sampled by the reference data-driven many-body potential in order to guarantee a faithful representation of the associated properties.</p>
Stream network from 1997 survey and 2008 LiDAR flight, Andrews Experimental Forest
HJ Andrews stream network from 1994 base prepared from Lienkaemper's 1976 stream survey field data and contours generated from GSC Digital Elevation Model (DEM) maps, and generated from 2008 LiDAR 1 meter bare earth DEM. The 1994 base matches the 30 and 10 meter DEMs and the 2008 LiDAR version matches the LiDAR base layers. Stream order was added to hf01302.
Daily precipitation from a network of weighing rain gauges on the Jornada Experimental Range (Jornada Basin LTER), 1976-2011.
This completed dataset contains daily precipitation measurements from a network of weighing rain gauges (Belfort Universal Precipitation Gauges, Series 5-780) at 58 locations on the Jornada Experimental Range in Dona Ana County, New Mexico, USA between January 1976 and December 2011. Locations and the dates during which data were collected were generally project-oriented. Temporal coverage per location is quite variable and ranges between 1,647 and 13,024 days. The network was maintained by USDA Agricultural Research Service personnel. After 2011 the remaining weighing rain gauges were replaced by tipping bucket gauges and those data are available separately.
Dataset from the paper entitled "Complex structure of molten FLiBe (2 LiF – BeF2) examined by experimental neutron scattering, X-ray scattering, and deep neural network-based molecular dynamics"
<p>Dataset from the paper entitled "Complex structure of molten FLiBe (2 LiF – BeF2) examined by experimental neutron scattering, X-ray scattering, and deep neural network-based molecular dynamics". These data include experimental total scattering measurements and molecular dynamics simulations on the molten structure of FLiBe. </p>
Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models
<p>#############</p> <p>Task-driven neural network models predict neural dynamics of proprioception, Cell 2024</p> <p>#############</p> <p>Authors: Marin Vargas, Alessandro (orcid=0000-0001-7073-4120) and Bisi, Axel (orcid=0009-0006-8602-7555) and Chiappa, Alberto Silvio (orcid=0009-0001-2764-6552) and Versteeg, Christopher (orcid=0000-0002-4269-5109) and Miller, Lee E. (orcid=0000-0001-8675-7140) and Mathis, Alexander (orcid=0000-0002-3777-2202)</p> <p>Affiliation: EPFL</p> <p>Date: January, 2024</p> <p>Link to the Cell article:</p> <p><a href="https://www.cell.com/cell/pdf/S0092-8674(24)00239-3.pdf">https://www.cell.com/cell/pdf/S0092-8674(24)00239-3.pdf</a></p> <p>--------------------------------</p> <p>Here we provide the neural data, activation and predictions for the best models and result dataframes of our article "Task-driven neural network models predict neural dynamics of proprioception".</p> <p>It contains the behavioral and neural experimental data (cuneate nucleus and somatosensory recordings from the Miller Lab, Northwestern University), the result dataframes for task-driven and untrained models, the activations and predictions for the *best models for all tasks* for active and passive movements and the predictions for linear models for active and passive movements. </p> <p>Note, the predictions of other models can be computed from the network weights that were deposited for all trained models. </p> <p>The overall structure of the data is:</p> <p>└── exp_analysis<br> ├── results - Contains the result dataframe of the predictions for all models, tasks and primates<br> ├── activations<br> │ ├── active - Contains activations related to active movements<br> │ └── passive - Contains activations related to passive movements<br> ├── predictions<br> │ ├── active - Contains predictions related to active movements<br> │ └── passive - Contains predictions related to passive movements<br> └── beh_exp_datasets<br> ├── matlab_data - Contains raw behavioral and neural data<br> ├── MonkeyAlignedDatasets_new - Contains padded test behavioral input for generating network activations<br> ├── MonkeyDatasets - Contains not aligned padded test behavioral input for generating network activations<br> ├── MonkeySpikeRegressDatasets - Contains datasets for training data-driven models<br> ├── MonkeySpikeRegressDatasets_new - Contains trial index for regression splits <br> └── new_beh_exp_dataframe - Contains pre-processed behavioral and neural data</p> <p>--------------------------------</p> <p>The activations and predictions for the best 3 models and for all tasks are stored in experiments folder (in .h5 format) that follows the same name convention of the checkpoints.</p> <p>The checkpoints are stored in experiment folders (experiment_***) that follow this scheme:<br>- Task: shallow exp id, deep TCNs exp id, LSTM id.</p> <p>Experiment IDs for each task:</p> <p>- Untrained: 15, 115, 45<br>- Classification: 4015, 5015, 4045</p> <p>- Torque: 8015, 8030, 8045</p> <p>- Regress joint pos: 17016, 17031, 17046<br>- Regress joint vel: 17216, 17231, 17246<br>- Regress joint pos & vel:: 17416, 17431, 17446<br>- Regress joint pos & vel & acc:: 20516, 20531, 20546</p> <p>- Regress hand pos: 4016, 5016, 4046<br>- Regress hand vel: 17316, 17331, 17346<br>- Regress hand pos & vel: 17516, 17531, 17546<br>- Regress hand pos & vel & acc: 20416, 17831, 17846</p> <p>- Regress hand and elbow pos: 20016, 20031, 20046<br>- Regress hand and elbow vel: 20916, 20931, 20946<br>- Regress hand and elbow pos & vel: 20616, 20631, 20646<br>- Regress hand and elbow pos & vel & acc: 20816, 20831, 20846</p> <p>- Redundancy reduction: 10020, 10035, 10050<br>- Autoencoder 20716 & 20717, 20731 & 20732, X</p> <p> </p> <p>The code to process the behavioral data is available at: <a href="https://github.com/amathislab/Task-driven-Proprioception/tree/master/exp_data_processing">https://github.com/amathislab/Task-driven-Proprioception/tree/master/exp_data_processing</a><br>The code to load and use the models to generate activations and predictions is available at: <a href="https://github.com/amathislab/Task-driven-Proprioception/tree/master/neural_prediction">https://github.com/amathislab/Task-driven-Proprioception/tree/master/neural_prediction</a></p> <p>To reproduce the results, it is possible to reproduce the main figures using the result dataframe. See our repository for more details. </p> <p>--------------------------------</p> <p>The datasets, weights, activations and predictions are released with Creative Commons Attribution 4.0 license.</p> <p>The code is released under the MIT license, see <a href="https://github.com/amathislab/Task-driven-Proprioception">https://github.com/amathislab/Task-driven-Proprioception</a></p> <p>If you find our code, weights, predictions or ideas useful, please cite:</p> <p>@article{vargas2024task,<br> title={Task-driven neural network models predict neural dynamics of proprioception},<br> author={{Marin Vargas}, Alessandro and Bisi, Axel and Chiappa, Alberto S and Versteeg, Chris and Miller, Lee E and Mathis, Alexander},<br> journal={Cell},<br> year={2024},<br> publisher={Elsevier}<br>}</p>
Group composition of individual personalities alters social network structure in experimental populations of forked fungus beetles
<p><span>Social network structure is a critical group character that mediates the flow of information, pathogens, and resources among individuals in a population, yet little is known about what shapes social structures. In this study, we experimentally tested whether social network structure depends on the personalities of group members. Replicate groups of forked fungus beetles (<i>Bolitotherus cornutus</i>) were engineered to include only members previously assessed as either more social or less social. We found that individuals behaved consistently across social contexts, exhibiting repeatable numbers of interactions and numbers of partners. At the group level, networks composed of more social individuals had higher interaction rates, higher tie density, higher global clustering, and shorter average shortest paths than those composed of less social individuals. We highlight group composition of personalities as a source of variance in group traits and a potential mechanism by which networks could evolve.</span></p>
Group and individual social network metrics are robust to changes in resource distribution in experimental populations of forked fungus beetles
<p>Social interactions drive many important ecological and evolutionary processes. It is therefore essential to understand the intrinsic and extrinsic factors that underlie social patterns. A central tenet of the field of behavioral ecology is the expectation that the distribution of resources shapes patterns of social interactions.</p> <p>We combined experimental manipulations with social network analyses to ask how patterns of resource distribution influence complex social interactions.</p> <p>We experimentally manipulated the distribution of an essential food and reproductive resource in semi-natural populations of forked fungus beetles (Bolitotherus cornutus). We aggregated resources into discrete clumps in half of the populations and evenly dispersed resources in the other half. We then observed social interactions between individually marked beetles. Half-way through the experiment, we reversed the resource distribution in each population, allowing us to control any demographic or behavioral differences between our experimental populations. At the end of the experiment, we compared individual and group social network characteristics between the two resource distribution treatments.</p> <p>We found a statistically significant but quantitatively small effect of resource distribution on individual social network position and detected no effect on group social network structure. Individual connectivity (individual strength) and individual cliquishness (local clustering coefficient) increased in environments with clumped resources, but this difference explained very little of the variance in individual social network position. Individual centrality (individual betweenness) and measures of overall social structure (network density, average shortest path length, and global clustering coefficient) did not differ between environments with dramatically different distributions of resources.</p> <p>Our results illustrate that the resource environment, despite being fundamental to our understanding of social systems, does not always play a central role in shaping social interactions. Instead, our results suggests that sex differences and temporally fluctuating environmental conditions may be more important in determining patterns of social interactions.</p>
Multilevel selection on social network traits differs between sexes in experimental populations of forked fungus beetles
<p>Both individual and group behavior can influence individual fitness, but multilevel selection is rarely quantified on social behaviors. Social networks provide a unique opportunity to study multilevel selection on social behaviors, as they describe complex social traits and patterns of interaction at both the individual and group levels. In this study, we used contextual analysis to measure the consequences of both individual network position and group network structure on individual fitness in experimental populations of forked fungus beetles (<em>Bolitotherus</em> <em>cornutus</em>) with two different resource distributions. We found that males with high individual connectivity (strength) and centrality (betweenness) had higher mating success. However, group network structure did not influence their mating success. Conversely, we found that individual network position had no effect on female reproductive success but that females in populations with many social interactions experienced lower reproductive success. The strength of individual-level selection in males and group-level selection in females intensified when resources were clumped together, showing that habitat structure influences multilevel selection. Individual and emergent group social behavior both influence variation in components of individual fitness but impact male mating success and female reproductive success differently, setting up intersexual conflicts over patterns of social interactions at multiple levels. </p>
Using generative adversarial networks to match experimental and simulated inelastic neutron scattering data
<p>Files uploaded here are related to the paper titled "Using generative adversarial networks to match experimental and simulated inelastic neutron scattering data". Here we investigate how generative adversarial networks can be used to match simulated- and experimental INS data.</p>
Group composition of individual personalities alters social network structure in experimental populations of forked fungus beetles
Open the record for dataset details and reuse information.
Group and individual social network metrics are robust to changes in resource distribution in experimental populations of forked fungus beetles
Open the record for dataset details and reuse information.
Multilevel selection on social network traits differs between sexes in experimental populations of forked fungus beetles
Open the record for dataset details and reuse information.
Stream stage and water table elevation in hyporheic and ground water from McRae Ck well network, Andrews Experimental Forest, 1989-1993
Measurements of stream stage of McRae Ck and water table heights from a network of shallow wells located adjacent to the stream. Data were collected from September 1989 to September 1992 on an irregular basis to sample both baseflow periods and storm events across seasons of the year. Stage height was read from staff plates permanently located at several location in the mainstem channel and in a small back channel; water table elevations were measured with a weighted measuring tape with a visible signal when the tape touched the water in the well.
ScienceDex guides
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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.