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101 results for “workload”
A Data-driven Analysis of a Cloud Data Center: Statistical Characterization of Workload, Energy and Temperature
<p>A characterization of cloud data center logs, analyzing its workload, energy and thermal characteristics. For more details of the dataset, please read the following paper: <a href="http://hpc.ec.tuwien.ac.at/files/UCC_23_data_center_analysis.pdf">http://hpc.ec.tuwien.ac.at/files/UCC_23_data_center_analysis.pdf.</a></p><p> </p><p>If you use the dataset, please cite the following work:</p><p>Shashikant Ilager, Adel N. Toosi, Mayank Raj Jha, Ivona Brandic, Rajkumar Buyya, "A Data-driven Analysis of a Cloud Data Center: Statistical Characterization of Workload, Energy and Temperature", In Proceedings of the 16th IEEE/ACM International Conference on Utility and Cloud Computing (UCC2023), Messina, Italy, December 4-7, 2023.</p>
Wrapper Impact Workloads and BSC Slurm Simulator Output of Dynamic Traces from CEA Curie
<p>This dataset contains the workloads, with the workflow added to them, and the results of the simulations of the dynamic trace utilizing <a href="https://www.cs.huji.ac.il/labs/parallel/workload/l_cea_curie/index.html">Curie's workload</a> carried out using <a href="https://ieeexplore.ieee.org/abstract/document/8641556">BSC's SLURM Simulator</a>.</p> <p>It is organized in two folders: workloads and results. In the first, we find a folder per target fair share value that the user that we track its usage. Within, we have a file with the name indicating if the workflow is wrapped or not, the type, vertical or horizontal, and the instant of submission. This file is in <a href="https://www.cs.huji.ac.il/labs/parallel/workload/swf.html">SWF</a> format. Under the results folder, we have the same organizaion: each .trace is the raw file produced by the simulator. </p> <p>The workload log from the CEA Curie system was graciously provided by Joseph Emeras (<a href="mailto:Joseph.Emeras@imag.fr">Joseph.Emeras@imag.fr</a>).</p>
Full Results from Simulations for Static and Dynamic Workloads Using BSC Slurm Simulator
<p>This dataset contains two CSV files. One is for the static runs, done by emulating LUMI's supercomputer environment, and dynamic, using CEA-Curie workload taken from Feitelson's repository.</p>
Replication data for: "How does Docker affect energy consumption? Evaluating workloads in and out of Docker containers"
<p>Database of raw power measurements and energy summaries for our Docker energy tests.</p> <p>Please cite us if you use this dataset.</p> <p>Schema</p> <pre><code>CREATE TABLE configuration( name TEXT PRIMARY KEY, description TEXT ); CREATE TABLE experiment( name TEXT PRIMARY KEY, description TEXT ); CREATE TABLE run( id PRIMARY KEY, configuration TEXT REFERENCES configuration(name) ON DELETE CASCADE ON UPDATE CASCADE, experiment TEXT REFERENCES experiment(name) ON DELETE CASCADE ON UPDATE CASCADE ); CREATE TABLE measurement( run REFERENCES run(id) ON DELETE CASCADE ON UPDATE CASCADE, timestamp REAL NOT NULL, -- Unix timestamp in milliseoncds power REAL NOT NULL ); CREATE TABLE energy( id PRIMARY KEY REFERENCES run(id), configuration TEXT REFERENCES configuration(name) ON DELETE CASCADE ON UPDATE CASCADE, experiment TEXT REFERENCES experiment(name) ON DELETE CASCADE ON UPDATE CASCADE, energy REAL NOT NULL, started REAL NOT NULL, ended REAL NOT NULL, elapsed_time REAL NOT NULL -- in milliseconds );</code></pre>
Reference Workloads for Traffic Generation
<p>Collection of traffic generator profiles, implemented for the needs of Superfluidity 5G project.</p> <p>Used in the context of the performance characterization (WP4) and validation (WP7) activities.</p>
Shopping in Immersive Virtual Reality: Effects of Visual, Auditory, and Cognitive Demands on Mental Workload
<p>The dataset - of the journal article "Shopping in Immersive Virtual Reality: Effects of Diminishing Visual, Auditory, and Cognitive Demands on Workload" - consists of heart rate and eye-tracking data per participant and experimental condition. It also contains the figures inserted in the manuscript, the MATLAB scripts, the Unity project of an immersive virtual supermarket, and a demo video of a participant performing a grocery task across the experimental conditions in the virtual supermarket.</p>
An EEG dataset for cross-session mental workload estimation: Passive BCI competition of the Neuroergonomics Conference 2021
<p>The dataset is part of a new open EEG database designed to answer a need for more publicly available EEG-based dataset to design and benchmark passive brain-computer interface pipelines (as detailed in [Hinss2021]). This database is currently being created and will be fully released before the end of the year. It will include data acquired over 30 participant, 4 tasks and 3 sessions. For this competition, hosted by the Neuroergonomics Conference 2021, only one task and half the participants will be analyzed. Hence, this competition focuses on a renowned task that elicits various levels of mental/cognitive workload: the Multi-Atribute Task Battery-II (MATB-II) developed by NASA (https://matb.larc.nasa.gov/). It is composed of 4 sub-tasks: system monitoring, tracking, resource management and communications. By varying the number and complexity of the sub-tasks, 3 levels of workload were elicited (verified through statistical analyzes of both subjective and objective -behavioral and cardiac- data). Each difficulty level was performed by 15 subjects (6 female; 9 average 25 y.o.) during 5 minutes per session, in a pseudo-randomized order. Each session was separated by 7 days. We used a 62 actiChamp EEG channels device (BrainProducts; electrode placement 10-20 system).</p> <p> </p> <p><strong>For the competition, your goal is to predict the mental workload for a given subject (intra-subject estimation) using the EEG data from another session (inter-session adaptation). More information on the conference website and in the documentation file.</strong></p>
Container Registry Benchmark experiments measurements and trace workload samples
<p>Measurements for experiments using Container Registry Benchmark, CReB. 4 experiments: Long running, small experiment stress mode, small experiment delay mode, and large workload experiment.</p> <p> </p> <p>Structure:</p> <ol> <li><strong>full-measurements-long-running-pull.csv : </strong>measurements for long running pull experiment</li> <li><strong>full-measurements-long-running-push.csv: </strong>measurements for long running push experiment</li> <li><strong>result-bug-analysis.zip: </strong>results from bug analysis of trace replayer</li> <li><strong>results-1hr-experiment.zip: </strong>measurements for the large experiment (4 registries)</li> <li><strong>results-small-delay.zip: </strong>measurements for the delay mode, small experiment with real workload</li> <li><strong>results-small-stress.zip: </strong>measurements for the stress mode, small experiment with real workload</li> <li><strong>traces.zip: </strong>traces used for pen-and-paper experiment, 1 hour sample, and the trace used for small experiment (selected are first 405 requests)</li> </ol>
Wrapper Impact Workloads and BSC Slurm Simulator Output of Static Traces based on Data from LUMI Supercomputer
<p>This dataset contains the workloads, with the workflow added to them, and the results of the simulations of the static trace utilizing LUMI fitted data carried out using <a href="https://ieeexplore.ieee.org/abstract/document/8641556">BSC's SLURM Simulator</a>.</p> <p>It is organized in two folders: workloads and results. In the first, we find a folder per experiment, which is a different randomly generated workload file. Within each experiment we find a folder per fair share inidicating the target platform, the workflow it was based on, and the characteristics of the tracked job: number of cores and runtime. The results folder follows the same scheme but with a file extension of ".trace".</p>
Asynchronous Workload Balancing through Persistent Work-Stealing and Offloading for a Distributed Actor Model Library
<p>With dynamic imbalances caused by both software and ever more complex hardware, applications and runtime systems must adapt to dynamic load imbalances. We present a diffusion-based, reactive, fully asynchronous, and decentralized dynamic load balancer for a distributed actor library. With the asynchronous execution model, features such as remote procedure calls, and support for serialization of arbitrary types, UPC++ is especially feasible for the implementation of the actor model. While providing a substantial speedup for small- to medium-sized jobs with both predictable and unpredictable workload imbalances, the scalability of the diffusion-based approaches remains below expectations in most presented test cases.</p> <p>Actor-UPCXX is a high-performance computing library based on the actor model to enable the use of the actor model for HPC simulations. The source code can be found at: https://github.com/TUM-I5/Actor-UPCXX</p>
Job workload data and transaction logs for the eBlocBroker
<p><strong>* What is eBlocBroker?</strong></p> <p>eBlocBroker is a blockchain based autonomous computational resource broker.</p> <p><strong>* Job workload data and transaction logs for the eBlocBroker</strong></p> <p>This repository contains job workload data and test results for the paper titled: <em>"eBlocBroker: A Blockchain Based Autonomous Computational Resource Broker"</em>. eBlocBroker and its driver programs are available from the following GitHub repository: <a href="https://github.com/ebloc/ebloc-broker">https://github.com/ebloc/ebloc-broker</a>.</p> <p>We first deployed our eBlocBroker contract on bloxberg. Then we tested eBlocBroker and the robustness of our Python scripts that allow provider and requester nodes to interact with each other through eBlocBroker and cloud storage services using two types of synthetic CPU workloads explained as follows:</p> <ol> <li>This workload helps to test running source code in combination with already cached and not cached datasets on the provider. As the source code, the <a href="https://dl.acm.org/doi/abs/10.1145/3330481">cppr</a> (colored parallel rush-relabel algorithm) is used, which runs with additional datasets. Three cppr processes run one after another with different randomly selected datasets. All four providers have the same 12 medium-size datasets, of which only 3 distinct ones from each other have lower prices. Two data files are the provider’s registered data, and one is from the requester’s local storage.</li> <li>The <a href="https://www.nas.nasa.gov/software/npb.html">NAS Parallel Benchmarks</a>, a small group of programs targeting the performance evaluation of parallel supercomputers. One of the NAS serialized benchmarks in Class B (Block Tridiagonal solver, Scalar Pentadiagonal solver, Unstructured Adaptive mesh, and Lower-Upper Gauss-Seidel solver) is selected randomly. Since providers’ prices are the same, the calculated cost for NAS jobs will be the same for all providers.</li> </ol> <p>In the test, our helper Python script maintains one hundred synthetic requesters within the requester node continually submit the following chosen workload randomly for 14 hours and 30 minutes.</p> <p>This record provides logs of clusters and clients, results of the submitted jobs on each cluster that is either completed of failed and their gained and returned fees, logs of the submitted jobs and their transaction hashes, and Slurm's job submission information, which are generated by the Driver programs.</p> <p><strong>** Transactions are taken from bloxberg (<a href="https://blockexplorer.bloxberg.org">https://blockexplorer.bloxberg.org</a>)</strong></p> <p>- Transactions deployed on the eBlocBroker Smart Contract:<br> <a href="https://blockexplorer.bloxberg.org/address/0xa0Fac3232234478E6A0d4d5564ed239c956A21f0/transactions">https://blockexplorer.bloxberg.org/address/0xa0Fac3232234478E6A0d4d5564ed239c956A21f0/transactions</a></p> <p>- <a href="https://blockexplorer.bloxberg.org/address/0x29e613B04125c16db3f3613563bFdd0BA24Cb629/transactions">Transactions of the <em>provider0_0x29e613B04125c16db3f3613563bFdd0BA24Cb629</em></a><br> <em>- </em><a href="https://blockexplorer.bloxberg.org/address/0x1926b36af775e1312fdebcc46303ecae50d945af/transactions">Transactions of the <em>provider1_0x1926b36af775e1312fdebcc46303ecae50d945af</em></a><br> - <a href="https://blockexplorer.bloxberg.org/address/0x4934a70Ba8c1C3aCFA72E809118BDd9048563A24/transactions">Transactions of the<em> provider2_0x4934a70Ba8c1C3aCFA72E809118BDd9048563A24</em></a> <br> - <a href="https://blockexplorer.bloxberg.org/address/0x51e2b36469cdbf58863db70cc38652da84d20c67/transactions">Transactions of the <em>provider3_0x51e2b36469cdbf58863db70cc38652da84d20c67</em></a> </p> <p><strong>* Files</strong></p> <p>Each provider contains <em>eudat, gdrive, ipfs, and ipfs_gpg </em>folders that contains the patch results that obtained from the named cloud storage.</p> <p>$ tree -L 2 .<br> ├── README.org<br> ├── base_test_eblocbroker<br> │ ├── NPB3.3-SER_source_code<br> │ ├── README.md<br> │ ├── _cppr<br> │ ├── cppr<br> │ ├── cppr_example.sh<br> │ ├── datasets<br> │ ├── run_cppr<br> │ ├── setup.sh<br> │ └── test_data<br> ├── check_list.org<br> ├── provider0_0x29e613B04125c16db3f3613563bFdd0BA24Cb629<br> │ ├── ebloc-broker<br> │ ├── eudat<br> │ ├── gdrive<br> │ ├── ipfs<br> │ ├── ipfs_gpg<br> │ ├── jobs_info_0x29e613b04125c16db3f3613563bfdd0ba24cb629.out<br> │ ├── result_ipfs_hashes.txt<br> │ ├── transactions_0x29e613B04125c16db3f3613563bFdd0BA24Cb629.csv<br> │ └── watch_0x29e613b04125c16db3f3613563bfdd0ba24cb629.out<br> ├── provider1_0x1926b36af775e1312fdebcc46303ecae50d945af<br> │ ├── ebloc-broker<br> │ ├── eudat<br> │ ├── gdrive<br> │ ├── ipfs<br> │ ├── ipfs_gpg<br> │ ├── jobs_info_0x1926b36af775e1312fdebcc46303ecae50d945af.out<br> │ ├── result_ipfs_hashes.txt<br> │ ├── transactions_0x1926b36af775e1312fdebcc46303ecae50d945af.csv<br> │ └── watch_0x1926b36af775e1312fdebcc46303ecae50d945af.out<br> ├── provider2_0x4934a70Ba8c1C3aCFA72E809118BDd9048563A24<br> │ ├── ebloc-broker<br> │ ├── eudat<br> │ ├── ipfs<br> │ ├── ipfs_gpg<br> │ ├── jobs_info_0x4934a70ba8c1c3acfa72e809118bdd9048563a24.out<br> │ ├── result_ipfs_hashes.txt<br> │ ├── transactions_0x4934a70Ba8c1C3aCFA72E809118BDd9048563A24.csv<br> │ └── watch_0x4934a70ba8c1c3acfa72e809118bdd9048563a24.out<br> ├── provider3_0x51e2b36469cdbf58863db70cc38652da84d20c67<br> │ ├── ebloc-broker<br> │ ├── eudat<br> │ ├── gdrive<br> │ ├── ipfs<br> │ ├── ipfs_gpg<br> │ ├── jobs_info_0x51e2b36469cdbf58863db70cc38652da84d20c67.out<br> │ ├── result_ipfs_hashes.txt<br> │ ├── transactions_0x51e2b36469cdbf58863db70cc38652da84d20c67.csv<br> │ └── watch_0x51e2b36469cdbf58863db70cc38652da84d20c67.out<br> ├── requesters<br> │ ├── ebloc-broker-logs<br> │ └── gdrive<br> └── transactions_contract_0xa0Fac3232234478E6A0d4d5564ed239c956A21f0.csv</p>
Accurate modeling and efficient QoS analysis of scalable adaptive systems under bursty workload
<p>The datasets include the traces used for the research and experiments on modelling and analyzing systems that execute under bursty workload:</p> <ul> <li>numReq10secondsfrom360000to660000-Paris contains a summary of the requests traces published in <a href="http://ita.ee.lbl.gov/html/contrib/WorldCup.html">http://ita.ee.lbl.gov/html/contrib/WorldCup.html</a> , by grouping into a single count the number of requests that servers in Paris region received every 10 seconds .</li> <li>mawi10seconds contains a summary of the traces published in <a href="http://mawi.wide.ad.jp/mawi/ditl/ditl2009/">http://mawi.wide.ad.jp/mawi/ditl/ditl2009</a> , by grouping the number of packets every 10 seconds into a single count. </li> </ul>
Data on the administrative workload and perceived administrative burden of farmers in Switzerland
<table> <tbody> <tr> <td> <p>We present data from a paper-and-pencil survey of Swiss farmers. The survey was mailed to 2,000 randomly selected Swiss farmers from the two largest Swiss language regions (German and French) in February 2019. A reminder was sent in April 2019. The response rate was around 40% (N = 808). In the main part of the survey, we collected quantitative data on farmers’ workload and perceived burden due to (1) overall farming activities, (2) administrative activities related to the application of direct payments, and (3) other office work related to farm planning, bookkeeping, purchasing, and sales. We also asked farmers to rate their current workload and perceived administrative burden compared to five years earlier. We also collected data on the perceived burden of using e-government services, the administrative workload of various voluntary direct payment schemes, and the workload of inspections and sanctions. We collected personal information about the farmers, such as age, education, work experience on the farm, work outside the farm, and their political activities (e.g. as a board or executive member in political or agricultural organisations). Finally, the farmers were asked to rate a series of statements regarding agricultural policy measures, the importance of inspection measures, the obligation to provide proof of eligibility for direct payments, information on current policy measures, and the justification of penalties for non-compliance with environmental or animal welfare standards. The survey results showed that, on average, Swiss farmers spent 3–5% of their total working time on administrative tasks. Based on a 60-hour working week, this means that, on average, farmers spent about 1.8–3 hours per working week on administrative activities. The farmers rated the perceived burden of administrative activities as higher than the burden of overall farming activities or other office work. The results also showed that the time spent on administrative activities and the associated perceived administrative burden had increased compared to five years earlier. Finally, the results showed that 28% of the Swiss farmers had received a penalty for non-compliance with direct payment regulations.</p> <p> </p> </td> </tr> </tbody> </table>
Generated Software Testing Workload
<p>This dataset represents a software testing workload generated from the distributions characterizing a real software testing workload. It consists of instance files, each of which is structured as follows:</p> <pre><code>[number of test suites] [test suite uuid] [test suite priority (1>0)] [arrival time (s)] [number of test cases] [test case uuid] [test case type] [test case outcome (0 - pass)] [test case duration (s)] [test case uuid] [test case type] [test case outcome (0 - pass)] [test case duration (s)] (...) [test suite uuid] [test suite priority (1>0)] [arrival time (s)] [number of test cases] [test case uuid] [test case type] [test case outcome (0 - pass)] [test case duration (s)] [test case uuid] [test case type] [test case outcome (0 - pass)] [test case duration (s)] (...) (...)</code></pre> <p> </p>
Generic and ML Workloads in an HPC Datacenter
<p>Updated Version of the <a title="previous upload" href="../records/13625495">previous upload</a>, adjusts node timestamps lacking behind at the beginning of the data collection.</p> <p>This archive contains hardware and workload traces from SURF Lisa, a Dutch datacenter consisting of 338 nodes, used by universities and researchers for various jobs. Around 85% of the nodes are equipped only with CPUs, handling generic compute-heavy workloads, the other 15% come with additional GPUs, serving as accelerators for Machine Learning (ML) jobs. Individual node hardware configurations are listed in `node_hardware_info.parquet`.</p> <p>Jobs within Lisa are submitted over the SLURM scheduler, where we logged job start and end time, resource allocation, and exit state for roughly 10 months (December 2021 to November 2022). This data saved in `slurm_table_cleaned.parquet`.</p> <p>Addidionally, we provide detailed Prometheus monitoring logs from all nodes over a timespan of 5 months (June 2022 to November 2022) in `prom_table_cleaned.parquet`. These logs contain over 90 attributes, including CPU/GPU power and temperatures, network I/O, memory and storage usage, and many more. These metrics are sampled at 30s intervals, resulting in a total of almost 130 million records across all nodes.</p> <p>Finally, job and node data are provided as a joined dataset in `prom_slurm_joined.parquet` for their 4 months of overlapping timespan. This combined data can provide more insights into the resource consumption and performance patterns of jobs.</p> <p>We conducted detailed analysis of this data where we specifically looked at the different characteristics of generic vs. ML workloads in a heterogeneous HPC environment. The pre-print of our analysis work can be found on <a href="https://arxiv.org/abs/2409.08949">arXiv</a>. Our code used for evaluation can be found on <a href="https://github.com/atlarge-research/2024-icpads-hpc-workload-characterization">GitHub</a>.<br><br></p> <table> <tbody> <tr> <th>Dataset Name</th> <th>Explanation</th> </tr> <tr> <td>slurm_table_cleaned.parquet</td> <td>Job data collected by SLURM</td> </tr> <tr> <td>prom_table_cleaned.parquet</td> <td>Node data collected by Prometheus</td> </tr> <tr> <td>prom_slurm_joined.parquet</td> <td>Joined Job and Node dataset</td> </tr> <tr> <td>node_hardware_info.parquet</td> <td>Hardware configurations of each node</td> </tr> </tbody> </table>
Analyzing the Impact of Workloads on Modeling the Performance of Configurable Software Systems (Supplementary Material)
<p>This repository provides supplementary material to the ICSE 2023 paper "Analyzing the Impact of Workloads on Modeling the Performance of Configurable Software Systems". We provide the following material:</p> <p>- The experimental setup, including the performance and measurement scripts.</p> <p>- (Aggregated) measurement data and configurations used in our analysis as well as the raw code coverage measurements.</p> <p>- An interactive dashboard to reproduce and reenact our analyses/findings, re-create all visualizations used in the original paper and those omitted due to space limitations.<br> <br> The repository is structured as follows:<br> <br> - accepted_paper.pdf: Camera-ready version of the original paper for reference.</p> <p>- coverages_raw.tar.gz: Raw coverage reports as compressed CSV files (uncrompressed: ~60 GB)</p> <p>- artifacts_excluding_raw_coverage.zip: aggregated measurement data, interactive dashboard, and experimental setup</p> <p>- README.md: A detailed documentation of all the material provided.</p>
COLET: A Dataset for Cognitive workLoad estimation based on Eye-Tracking
<p>Cognitive workload is an important component in performance psychology, ergonomics, and human factors. Unfortunately, benchmarks and publicly available datasets are scarce, making it difficult to establish new approaches and comparative studies. In this work, COLET-COgnitive workLoad state estimation based on Eye-Tracking dataset is presented. Forty-seven (47) individuals' eye movements were monitored as they solved puzzles involving visual search tasks of varying complexity and duration. The authors give an in-depth study of the participants' performance during the experiments while eye and gaze features were derived from low-level eye recorded metrics, and their relationships with the experiment tasks were investigated. Finally, the results from the classification of cognitive workload levels solely based on eye and gaze data, by employing and testing a set of machine learning algorithms are provided. The dataset is made available to the public.</p> <p> </p> <p>Please cite the following work: </p> <p>Ktistakis, E., Skaramagkas, V., Manousos, D., Tachos, N. S., Tripoliti, E., Fotiadis, D. I., & Tsiknakis, M. (2022). Colet: A dataset for cognitive workload estimation based on eye-tracking. <em>Computer Methods and Programs in Biomedicine</em>, 106989. https://doi.org/10.1016/j.cmpb.2022.106989</p>
DATA7: A dataset that uses synthetic trajectories of vehicles and real cellular tower locations to simulate the workload of Edge nodes in the city of Pisa
<p><strong>Description</strong></p> <p>The dataset contains observations of vehicles in the range of edge nodes (cellular towers). The trajectories of vehicles are synthetically generated with <a href="https://www.eclipse.org/sumo/">SUMO</a>. The cellular tower positions have been taken from <a href="https://opencellid.org/">OpenCelliD</a>. The dataset is in the comma-separated values (CSV) format, and is around 220MB decompressed.</p> <p><br> The CSV contains the following fields:<br> * edge_id: unique identifier of the edge devices<br> * edge_lat: latitude coordinate of the edge device<br> * edge_lon: longitude coordinate of the edge device<br> * time: simulation step of the observation<br> * vehicle_id: unique identifier of the vehicle<br> * vehicle_lat: latitude coordinate of the vehicle<br> * vehicle_lon: longitude coordinate of the vehicle<br> * distance: geodesic distance in meters from the vehicle and the edge device</p>
"I was on vacation in Mala, living in a self-catering holiday cottage, together with my girlfriend (we're together for 11 years now), recovering from a heavy workload in the second half of 2008. We were sitting outside, probably sipping a beer, when we heard the sound of bells approaching. Stepping on the stones that enclose the little forecourt of the cottage, we could just see the goat herd being driven by. Idashed for my R09 (recording equipment) to get that impression – but too slowly too late, it seemed, the herd had disappeared and with it the sound. When Iwas about to pack my R09 again the sound appeared to come back, so Idashed down the driveway, just in time to see the herd pass, and then Ifollowed it a couple of hundred meters, walking behind the herd, trying not to breathe or make stepping sounds, eventually, when dogs started barking and a car approached from behind, I stopped and let the goats go on, the car passes, honks ... and Icut the recording and walk back to the cottage." [Peter/ptroxler]13 in Collecting Sounds. Online Sharing of Field Recordings as Cultural Practice
"I was on vacation in Mala, living in a self-catering holiday cottage, together with my girlfriend (we're together for 11 years now), recovering from a heavy workload in the second half of 2008. We were sitting outside, probably sipping a beer, when we heard the sound of bells approaching. Stepping on the stones that enclose the little forecourt of the cottage, we could just see the goat herd being driven by. Idashed for my R09 (recording equipment) to get that impression – but too slowly too late, it seemed, the herd had disappeared and with it the sound. When Iwas about to pack my R09 again the sound appeared to come back, so Idashed down the driveway, just in time to see the herd pass, and then Ifollowed it a couple of hundred meters, walking behind the herd, trying not to breathe or make stepping sounds, eventually, when dogs started barking and a car approached from behind, I stopped and let the goats go on, the car passes, honks ... and Icut the recording and walk back to the cottage." [Peter/ptroxler]13
Collection of runs with VIA genomics workload on RISC-V architectures
<p>This is a repository of results obtained experimenting with VIA genomics workload on RISC-V architectures. The current repository will be moved into a proper web-site with tools to properly visualize the data gathered. However, for now, we provide the results as txt files with the output of the workload for the several cases we examined so far. Within the dataset there is a README explaining the nomenclature used to save the results.</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.