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251 results for “log data”
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>
Game Data Event Log from Age of Empire Interactions
<p><span>The event log describes players' behavior in the real-time strategy game Age of Empires. Each case describes the events that a player triggers in a game. </span><span>There are 185.094 cases that consist</span><span> of more than 18 million events. The timestamp represents the elapsed time since the start of the game.</span></p> <p><span>Each player is assigned an</span><span> Elo ranking that is higher, the better the player is. This allows us to study the implications of skill on players' behavior. Also, games can take place on different maps, influencing the situations the players find themselves in. Some games follow clear initial strategies, which are called build orders. These build orders are comparable to chess openings.</span></p> <p><span>The event log is split into ten parts to make the import feasible for smaller machines.</span></p>
Execution and data log: on the effectiveness of random and adaptive random test case prioritization
<p>Execution and data logs automatically generated by the test driver during experimentation. These results are similar (but not identical) to the results summarized in Table 1 of the following paper:</p> <p>Z. Q. Zhou, A. Sinaga, and W. Susilo, "On the fault-detection capabilities of adaptive random test case prioritization: case studies with large test suites," in Proceedings of the 45th Annual Hawaii International Conference on System Sciences (HICSS-45). IEEE, 2012, pp. 5584-5593.<br> https://doi.org/10.1109/HICSS.2012.454</p> <p>Compared with the above paper, the current dataset was collected from a different but similar set of experiments and included faulty versions 13, 23, and 26 of the Replace program---although these three versions were excluded from the experiments reported in the above paper due to their instability.</p> <p>If you find these data useful, please cite the above paper.</p>
HYPERCOG_Process data_Welding log_2020-07-30
<p>The HyperCOG project addresses the full digital transformation of process industry through an innovative Industrial Cyber-Physical System and Data Analytics. It is based on advanced technologies that enable the development of a hyperconnected network of digital nodes. The nodes can catch outstanding streams of data in real-time, which together with the high computing capabilities, provide sensing, knowledge and cognitive reasoning, making companies robust in the face of variant scenarios. The breaking-edge system proposed in this work is validated on productivity, environmental and replicability aspects on three use cases of three di_erent sectors: steel, cement and chemical.</p> <p>Participating entities: LORTEK. The data was gathered and used in the proof of concept of the architecture introduced in the paper in the way that is described in it.</p> <p><strong>Dataset 1: Welding process data (xlsx files)</strong></p> <p>Real time data of the process of a welding cell on Excel sheets. The data is obtained at a frequency of 100 Hz and variables of voltage, current, temperature, gas flux, etc. are registered in the Excel file by rows.</p> <p><strong>Dataset 2: Temperature and movement of the piece constructed (zip files)</strong></p> <p>Data of temperatures obtained by thermocouple sensors and distortion of the structure measured by a laser sensor. The zip files contain coma separated values of 8 thermocouples welded to the substrate of the piece constructed by the welding cell. The reading of a laser sensor is also recorded along the x coordinate of the movement of the robot for synchronization purposes.</p> <p>The article corresponding to these datasets are available in open access in </p> <pre><a href="https://doi.org/10.5281/zenodo.5533904">https://doi.org/10.5281/zenodo.5533904</a></pre>
Logging data at Site ITA-1
<p>This dataset is logging data at Site ITA-1, which is located on the Kii Peninsula, southwest Japan.</p> <p>Drilling was conducted by Geological Survey of Japan, AIST.</p> <p>Please refer Kiguchi et al. (2014) for details.</p> <p>ITA_res_gr.csv includes depth [m], resistivity [ohm m], and natural gamma ray [API].</p> <p>ITA_vp_vs.csv includes depth [m], Vp [km/s], and Vs [km/s].</p> <p> </p> <p> </p>
Data for: Temporal variations in female moose responses to roads and logging in the absence of wolves
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Data from: eDNA metabarcoding of log hollow sediments and soils highlights the importance of substrate type, frequency of sampling and animal size, for vertebrate species detection
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Data from: Fine-scale reconstruction of pelagic fish migration by iso-logging of eye lens
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NEON lakes logged multisonde data
This repository contains logged multisonde water quality data from National Ecological Observatory Network (NEON) lake sites. As of 1 May 2023 this data has not yet been ingested into NEON's data processing pipeline or published on the NEON data portal (data.neonscience.org). It is being provided to help fill these gaps until it can be ingested. This is raw, level 0 data which has not been QAQC'ed, and NEON makes no guarantees regarding its quality. Any questions regarding this data should be directed to the NEON staff listed in the Contacts.
Marsh water table height, logging data from the railroad Spartina marsh site on the Parker River for April-November 2012.
Measurements of water table height in the Parker River marsh located downstream of the railroad bridge. Measurements were taken every 5 minutes at each logger along a transect of water level loggers running perpendicular to the Parker River bank at the railroad site, MAR-PR-Wtable-RR for April-November 2012.
Marsh water table height, logging data from the railroad Spartina marsh site on the Parker River for March-November 2013.
Measurements of water table height in the Parker River marsh located downstream of the railroad bridge. Measurements were taken every 5 minutes at each logger along a transect of water level loggers running perpendicular to the Parker River bank at the railroad site, MAR-PR-Wtable-RR for Mar-November 2013.
Marsh water table height, logging data from the Typha marsh site on the upper Parker River for April-November 2012.
Measurements of water table height in the upper Parker River Typha sp. marsh. Measurements were taken every 5 minutes at each logger along a transect of water level loggers running perpendicular to the Parker River bank at the Typha site, MAR-PR-Wtable-T, for April - November 2012
Marsh water table height, logging data from the Typha marsh site on the upper Parker River for March-November 2013.
Measurements of water table height in the upper Parker River Typha sp. marsh. Measurements were taken every 5 minutes at each logger along a transect of water level loggers running perpendicular to the Parker River bank at the Typha site, MAR-PR-Wtable-T, for Mar - November 2013
Marsh water table height, logging data from the Typha marsh site on the upper Parker River for April-November 2014.
Measurements of water table height in the upper Parker River Typha sp. marsh. Measurements were taken every 5 minutes at each logger along a transect of water level loggers running perpendicular to the Parker River bank at the Typha site, MAR-PR-Wtable-T, for April - November 2014
Marsh water table height, logging data from the railroad Spartina marsh site on the Parker River for April-November 2015.
Measurements of water table height in the Parker River marsh located downstream of the railroad bridge. Measurements were taken every 5 minutes at each logger along a transect of water level loggers running perpendicular to the Parker River bank at the railroad site, MAR-PR-Wtable-RR for April-October 2015.
Marsh water table height, logging data from the Typha marsh site on the upper Parker River for April-November 2015.
Measurements of water table height in the upper Parker River Typha sp. marsh. Measurements were taken every 5 minutes at each logger along a transect of water level loggers running perpendicular to the Parker River bank at the Typha site, MAR-PR-Wtable-T, for April - November 2015
Data to "Near-optimal combination of disparity across a log-polar scaled visual field"
<p>This record contains experimental and analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in:</p> <p>Maiello, G., Chessa, M., Bex, P. J., & Solari, F. (2020) Near-optimal combination of disparity across a log-polar scaled visual field. <em>PLOS Computational Biology, 16</em>(4), e1007699. <a href="https://doi.org/10.1371/journal.pcbi.1007699">https://doi.org/10.1371/journal.pcbi.1007699</a></p>
IODP Expedition 392, Hole U1579D - Well Logging Data
Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
IODP Expedition 392, Hole U1581B - Well Logging Data
Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
IODP Expedition 392, Hole U1580A - Well Logging Data
Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
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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.