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3 results for “SIMBED+”

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edi56/100

Seasonal Ice Mass-balance Buoy (SIMB) measurements from sites along the Beaufort Sea Coast, Alaska, 2018-ongoing

Measurements of the thickness of sea ice and the depth of its snow cover allow us to calculate how their mass changes in response to the varying fluxes of heat between the ocean and atmosphere over the course of a season. Repeated drill measurements are not ideal for this purpose since each drill hole disturbs the ice and its insulating snow cover. Also, spatial variability in ice thickness can mask temporal changes if holes are not drilled in the same place each time. Hence, methods that do not require re-drilling are preferred. Automated systems such as the Seasonal Ice Mass-balance Buoy (SIMB; Planck et al, 2019) provide high temporal resolution for capturing sub-daily variations and typically include sensor strings to measure the vertical temperature profile from the air to the ocean, which can be used to infer other properties of the ice cover such as strength and porosity. Under the Beaufort Lagoon Ecosystems LTER (BLE LTER) research program, several SIMBs are deployed at sites along the Beaufort Sea coast and record a suite of parameters including but not limited to snow depth, ice thickness, position of ice surface and bottom, water/air temperature, and vertical profiles of temperature. Planck, C. J., J. Whitlock, C. Polashenski, and D. Perovich (2019), The evolution of the seasonal ice mass balance buoy, Cold Regions Science and Technology, 165, 102792, doi: https://doi.org/10.1016/j.coldregions.2019.102792.

openCC0Mar 2021View details →

zenodo44/100

SIMBED - Offline Real-World Wireless Networking Experimentation using ns-3

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. 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. 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. 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? 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. The SIMBED 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. 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.   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.   The following dataset presents the results of the SIMBED project, organized in different folders, for each subset of experiments carried on: <ul> <li>SubExp#1: Static point-to-point Wi-Fi communications using auto-rate (Minstrel) <ul> <li>SubExp#1.1: Using w-iLab.2 (medium to high SNR scenarios)</li> <li>SubExp#1.2: Using w-iLab.2 (low SNR scenarios)</li> <li>SubExp#1.3:  Using NITOS</li> <li>SubExp#1.4: Using w-iLab.1 (datacenter room)</li> </ul> </li> <li>SubExp#2: Static point-to-point Wi-Fi communications using fixed rate</li> <li>SubExp#3: Mobile point-to-point Wi-Fi communications using auto-rate (Minstrel)</li> <li>SubExp#4: Static multiple access Wi-Fi communications using auto-rate (Minstrel) <ul> <li>SubExp#4.1: Using w-iLab.2 (bidirectional) (medium to high SNR scenarios)</li> <li>SubExp#4.2: Using w-iLab.2 (bidirectional) (low SNR scenarios)</li> <li>SubExp#4.3: Using NITOS (bidirectional)</li> <li>SubExp#4.4: Using w-iLab.1 (bidirectional)</li> <li>SubExp#4.5: Using NITOS (2 STAs)</li> <li>SubExp#4.6: Using w-iLab.2 (2 STAs)</li> </ul> </li> <li>SubExpExample: 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> 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 parsed experimental results, which contains This folder contains the details and parsed logs of the experiment, as follows: <ul> <li>date_time.cfg – configuration details of the experiment</li> <li>date_time_NodeID<a href="#_ftn1">[1]</a>_SenderID<a href="#_ftn2">[2]</a>_ReceiverID<a href="#_ftn3">[3]</a>_FlowType<a href="#_ftn4">[4]</a>_Params<a href="#_ftn5">[5]</a>.snr – logs of the Signal/Noise ratio (1 file per node/flow)  </li> <li>date_time_NodeID_SenderID_ReceiverID_FlowType_Params.stats – logs of the packets received (1 file per node/flow)  </li> <li>NodeID.waypoints – coordinates of the static nodes</li> <li>date_time_MobileNodeID.waypoints – waypoints of the mobile nodes (when applicable)</li> </ul> The experiment’s folder also contains a folder for the simulations output with the simulations statistics files, for the multiple simulations approaches considered, as follows: <ul> <li>date_time_NodeID_SenderID_ReceiverID_FlowType_Params.simstats – logs of the packets received (simulation)</li> </ul>   <a href="#_ftnref1">[1]</a> ID of the node Logging node <a href="#_ftnref2">[2]</a> ID of the Sender node <a href="#_ftnref3">[3]</a> ID of the Receiver node <a href="#_ftnref4">[4]</a> Flow type: Unidirectional, Bidirectional or Unidirectional with Multiple Access <a href="#_ftnref5">[5]</a> Configurable parameters: Sender/Receiver Transmission Power and Data Rate (when applicable)

opencc-by-4.0Apr 2019View details →

zenodo36/100

SIMBED+ - Replicable Real Wireless Networking Experiments using ns-3

Wireless networking R&D depends on experimentation to make realistic evaluations of networking solutions, as simulation is inherently a simplification of the real-world. However, despite more realistic, experimentation is limited in aspects where simulation excels, such as repeatability and reproducibility. Real wireless experiments may be difficult to repeat. For the same input they can produce very different output results, since wireless communications are influenced by external phenomena such as noise, interference, and multipath. Even if repeatable, experiments may still be difficult to reproduce. Namely, other researchers may be unable to reproduce an experiment and confirm previous experimental results right away, because either the testbed is unavailable – offline or running other experiments when using a community testbed –, or inaccessible at all when a custom testbed was originally used. Fed4FIRE+ testbeds such as w-iLab.t, although operating in controlled environment, do not fully address the problem. This is even more evident in testbeds running in non-controlled and very dynamic environments, such as CityLab, where they may suffer from radio interference and competition from existing networks sharing the same radio spectrum. What if we could make any wireless experiment repeatable and reproducible? 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? INESC TEC has developed 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. The previous SIMBED project validated the OE approach for controlled environments and helped identifying some of its limitations. The OE approach was then improved to support experiments using Multiple-In-Multiple-Out (MIMO) and shared radio spectrum with concurrent networks. To further validate the improved OE approach, the SIMBED+ project aimed at running a set of experiments on top of the controlled and non-controlled environments of w-iLab.t and CityLab Fed4FIRE+ testbeds. For that purpose, we configured different fixed experimental scenarios, representative of Wi-Fi range of operation, subjected to controlled and non-controlled interference from concurrent experiments. For each experiment the achieved network throughput was measured. Then, we repeated each experiment using, both, Pure Simulation (PS) and OE approaches (now with MIMO and channel occupancy information) based on ns-3, also measuring the network throughput for the same set of experiments. To compare the network throughput of the real experiments with their PS and OE counterparts, Cumulative Distribution Function (CDF) curves were used, plotting all 1-second average throughput samples. For all the experiments performed in SIMBED+, using the improved OE approach resulted in network throughput considerably closer to real than using the PS approach. This is even more evident in non-controlled environments using MIMO and shared radio spectrum. These results were important for further validating the OE approach, producing two conference papers and one journal paper. The SIMBED+ results increased our confidence on the OE approach ability of reproducing past experiments, and are envisioned to foster the adoption of the OE approach by the networking community, in complement to the use of real experimentation.   The following dataset presents the results of the SIMBED+ project, organized in different folders, for each subset of experiments carried on: <ul> <li>SubExp#1: Trace-based PHY rate with SISO support (802.11a, BW 20 MHz) </li> <li>SubExp#2: Trace-based PHY rate with MIMO support (802.11n/ac, BW 20/40 MHz, MIMO 3x3) <ul> <li>SubExp#2.1: IEEE 802.11n, 20 MHz, MIMO 3x3</li> <li>SubExp#2.2: IEEE 802.11n, 40 MHz, MIMO 3x3</li> <li>SubExp#2.3: IEEE 802.11ac, 40 MHz, MIMO 3x3</li> </ul> </li> <li>SubExp#3: Shared radio spectrum support (occupancy at the sender)</li> <li>SubExp#4: Shared radio spectrum support (occupancy at the receiver)</li> <li>SubExp#5: Trace-based PHY rate, MIMO and shared radio spectrum support</li> </ul> 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 parsed experimental results, which contains This folder contains the details and parsed logs of the experiment, as follows: <ul> <li>date_time.cfg – configuration details of the experiment</li> <li>date_time_NodeID<a href="#_ftn1">[1]</a>_SenderID<a href="#_ftn2">[2]</a>_ReceiverID<a href="#_ftn3">[3]</a>_FlowType<a href="#_ftn4">[4]</a>_Params<a href="#_ftn5">[5]</a>.snr – logs of the Signal/Noise ratio (1 file per node/flow)  </li> <li>date_time_NodeID_SenderID_ReceiverID_FlowType_Params.stats – logs of the packets received (1 file per node/flow)  </li> <li>NodeID.waypoints – coordinates of the static nodes</li> <li>date_time_MobileNodeID.waypoints – waypoints of the mobile nodes (when applicable)</li> </ul> The experiment’s folder also contains a folder for the simulations output with the simulations statistics files, for the multiple simulations approaches considered, as follows: <ul> <li>date_time_NodeID_SenderID_ReceiverID_FlowType_Params.simstats – logs of the packets received (simulation)</li> </ul>   <a href="#_ftnref1">[1]</a> ID of the node Logging node <a href="#_ftnref2">[2]</a> ID of the Sender node <a href="#_ftnref3">[3]</a> ID of the Receiver node <a href="#_ftnref4">[4]</a> Flow type: Unidirectional, Bidirectional or Unidirectional with Multiple Access <a href="#_ftnref5">[5]</a> Configurable parameters: Sender/Receiver Transmission Power and Data Rate (when applicable)

opencc-by-4.0Mar 2020View details →

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

Annotated Behaviour and Observability Dataset (ABODe)

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abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record

Find research datasets worth reusing

Dataset results

Seasonal Ice Mass-balance Buoy (SIMB) measurements from sites along the Beaufort Sea Coast, Alaska, 2018-ongoing

SIMBED - Offline Real-World Wireless Networking Experimentation using ns-3

SIMBED+ - Replicable Real Wireless Networking Experiments using ns-3

Understand access before you commit

Allen Brain Atlas

Annotated Behaviour and Observability Dataset (ABODe)

DANDI Archive for NWB datasets

International Brain Laboratory public data

OpenNeuro

Your academic CV and NIH biosketch