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1,393 results for “Trace”
Chameleon bare metal cloud traces (2020-09-03)
<p>This cloud trace contains instance events and machine events for the bare-metal clouds on the Chameleon testbed since it was built in 2015. Several text fields have been anonymized to hide user information.</p> <ul> <li>This trace contains data from 2015-06-17T17:54:29.000Z until 2020-09-03T01:51:34.000Z. </li> <li>The epoch time for timestamps is 2015-06-17T00:00:00.</li> <li>It follows <strong>version 0.3</strong> of the <a href="http://press3.mcs.anl.gov/scienceclouds/cloud-traces/cloud-trace-format/">cloud trace format</a>.</li> </ul> <p>You can find more information and trace datasets at <a href="https://www.scienceclouds.org/cloud-traces">Science Clouds</a>.</p>
Chameleon KVM cloud traces (2020-09-04)
<p>This cloud trace contains Nova compute events for the OpenStack KVM cloud on the Chameleon testbed since it was built in 2015. Several text fields have been anonymized to hide user information.</p> <ul> <li>This is the last set of traces for the KVM cloud built in 2015 in a file "chameleon_legacy_kvm_tacc_2020_02_03.zip". Chameleon deprecated the legacy KVM site in February 2020 and future versions of this deposition will not include this historical trace data.</li> <li>This trace contains data from 2015-09-17T15:37:05.000Z until 2020-02-03T06:23:40.000Z.</li> <li>The epoch time for timestamps is 2015-09-06T00:00:00.</li> <li>It follows version 0.3 of the <a href="http://press3.mcs.anl.gov/scienceclouds/cloud-traces/cloud-trace-format/">cloud trace format</a>.</li> </ul> <p>You can find more information and trace datasets at <a href="https://www.scienceclouds.org/cloud-traces">Science Clouds</a>.</p>
Isotopes and related data associated with water tracing with environmental DNA in a high-Alpine catchment
<p>Isotopes and related data associated with water tracing with environmental DNA in a high-Alpine catchment<br> Prepared by Natalie Ceperley, February 2020. </p> <p><br> All methods associated with this data are available in the manuscript: Elvira Mächler, Anham Salyani, Jean-Claude Walser, Annegret Larsen, Bettina Schaefli, Florian Altermatt, and Natalie Ceperley. 2019. Water tracing with environmental DNA in a high-Alpine catchment, Hydrology and Earth System Sciences. https://doi.org/10.5194/hess-2019-551. <br> Related data sets are and will be published in the Vallon de Nant Community on Zenodo. Associated sequencing data are publicly available on European Nucleotide Archive (Mächler et al., 2020). </p> <p>All isotope data analyzed in the laboratory of Torsten W. Vennemann at the University of Lausanne. </p> <p> </p> <p><br> All Files:<br> ▪ NaN - No measurement or sample<br> ▪ Details regarding measurement are available in paper or supplement. </p> <p>Files: <br> 1) climate_hydro_2017_daily.csv <br> ⁃ 16 columns: <br> ⁃ 1. day of year with January 1, 2017 = 1<br> ⁃ 2-5. Q: daily mean, min, max, and baseflow discharge as measured at outlet (location ER/MR), in liters / day <br> ⁃ 6. P: mean mm of rain across catchment per day<br> ⁃ 7. SR: total solar radiation per day in W/hr/m2 as median of 4 meteorological stations<br> ⁃ 8-10. SCA: mean, min, and max snow covered area on days with satellite imagery available for whole catchment area, in %<br> ⁃ 11-13. water temperature, mean, min, and max, at outlet (location ER/MR), in degrees C<br> ⁃ 14-16. air temperature, mean, min, and max at 4 meteorological stations, in degrees C</p> <p>2) delta-18-O_permil.csv <br> ⁃ stable isotopes of water (delta 18-O) in per mil<br> ⁃ columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)</p> <p>3) delta-2-H_permil.csv <br> ⁃ stable isotopes of water (delta 2-H) in per mil<br> ⁃ columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)</p> <p>4) dqdt_outlet_prev48hrs.csv<br> - dq/dt determined at the outlet for the previous 48 hours at sampling moment (TimeOfSamples_HR.csv) for each sampling site<br> ⁃ columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)</p> <p><br> 5) ednasamplecount.csv <br> - this is the tally of samples (1 sample includes 4 replicates)<br> ⁃ columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)</p> <p>6) electricalconductivity_instrument.csv <br> ⁃ Code: <br> 108 - post-analyzed using a glass bodied 6 mm probe in the laboratory (Jenway 4510, Staffordshire, UK). <br> 102 - hand measurement with WTW (multi-3510 with a IDS-tetracon-925, Xylem Analytics, Germany)<br> ⁃ columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)</p> <p><br> 7) electricalconductivity_uScm.csv <br> - this is the electrical conductivity in micro siemens per cm, according to the instruments coded in electricalconductivity_instrument.csv<br> ⁃ columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)</p> <p>8) LC-excess.csv <br> - this is the line control execss from the meteoric water line as determined by the samples in the file: precipitationistopemetadata.csv<br> ⁃ columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)</p> <p>9) locations.csv <br> ⁃ Location codes used in other files. <br> - Coordinates in CH1903 / LV03 and WGS 84 (lat/lon). Elevation in m. asl. </p> <p>10) precipitationisotopemetadata.csv <br> - This is the sampling information for the isotope data that was used to calculate the meteoric water line. <br> - The full data set will become available in a subsequent publication on Zenodo linked to the same community. <br> - 4 columns: <br> - 1. code: rain (1) or snow (2)<br> - 2. collection date and time<br> - 3. elevation in m. asl. <br> - 4. in the case of rain, this is the depth of collection in mm (area normalized volume), in the case of snow, this is the mean depth below the surface that the sample was taken from in cm. <br> ⁃ columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)</p> <p>11) sampledates.csv <br> - These are the sample dates in day, month, year and day of year corresponding to the rows in other files</p> <p>12) stationlocations.csv<br> - These are the locations of four meteorological stations and discharge measurement station. <br> - Coordinates in CH1903 / LV03 and WGS 84 (lat/lon). Elevation in m. asl. </p> <p>13) TimeOfSamples_HR.csv <br> - This is the time of the sample in hours and decimals correspond to minutes past hour<br> ⁃ columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)</p> <p>14) watertemperature_degC.csv <br> ⁃ columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)<br> - measure in degrees C<br> - instrument in watertemperature_instrument.csv</p> <p>15) watertemperature_instrument.csv <br> ⁃ Code: <br> 1 = hand measurement with WTW (multi-3510 with a IDS-tetracon-925, Xylem Analytics, Germany)<br> 2 = HOBO Pendant Temperature/Light Data Logger 64K - UA-002-64", Onset (Bourne, MA, USA)<br> 3 = Continually logging WTW (IDS-tetracon-325, Xylem Analytics, Germany)<br> 4 = Continually logging (10min) HOBO U24-001 Conductivity, Onset (Bourne, MA, USA) <br> ⁃ columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)</p>
Microscopic vehicular mobility trace of Europarc roundabout, Creteil, France (vehicular-mobility-trace.github.io: v1.0)
<p>First release of the Europarc roundabout micro mobility dataset, Creteil, France.</p> <p>http://vehicular-mobility-trace.github.io/</p>
Dataset for the project "Evaluation of the effects of trace elements from street dust under urban – industrial conditions on the ecophysiology of Acer platanoides L. and Tilia cordata Mill.
<p>Description of the project: The rapid growth of cities, industry and transport has significantly deteriorated environmental quality, especially in areas with the highest population densities. It applies to water, soil, and air, especially in urban areas. Air pollutants include particulate matter (PM), which harms human health. According to WHO reports (2021), PM pollution is the cause of cardiovascular and respiratory diseases, leading to 4.2 million premature deaths worldwide in 2016. Although improving every year, the situation in Poland is still worse than in many European countries. The particulate matter also includes heavy metals, which have a toxic effect on plants. Plants in urban areas are particularly vulnerable, especially trees, which perform several vital functions, including mitigating climate change, filtering pollutants, and improving air quality. The aim of the project was to determine and compare the morphological and physiological responses of selected tree species to particulate pollution stress under urban conditions. Tree leaves are an essential barrier to atmospheric dust by trapping it on their surface. However, this may come at the cost of reduced light absorption, increased leaf temperature, damage to leaf blades and consequently impaired photosynthesis and plant productivity. However, the ability to absorb dust varies between tree species. It depends on the leaf surface structure, and the response may be due to the species' sensitivity to pollutants. Investigations were conducted in the Upper Silesian Industrial Area around various emission sources, such as heavy metal smelters, combined heat and power plants, and busy streets. The research focused on two tree species common in urban areas, the Norway maple (<i>Acer platanoides</i>) and the small-leaved lime (<i>Tilia cordata</i>). It included measurement of heavy metal concentrations in leaf blades and dust collected on their surface, analysis of concentrations of selected pigments and ascorbic acid as markers of environmental stress. The study provided a preliminary assessment of the impact of particulate pollution on tree function under harsh urban conditions and determined the potential of the studied species to reduce atmospheric dust.</p>
Improved Converted Traces from Rebasing Microarchitectural Research with Industry Traces
<p>Improved converted traces of the paper "Rebasing Microarchitectural Research with Industry Traces", published at the 2023 IEEE International Symposium on Workload Characterization. It includes the CVP-1 traces used in the paper converted with our improved converter.</p><p><i>Abstract</i>: Microarchitecture research relies on performance models with various degrees of accuracy and speed. In the past few years, one such model, ChampSim, has started to gain significant traction by coupling ease of use with a reasonable level of detail and simulation speed. At the same time, datacenter class workloads, which are not trivial to set up and benchmark, have become easier to study via the release of hundreds of industry traces following the first Championship Value Prediction (CVP-1) in 2018. A tool was quickly created to port the CVP-1 traces to the ChampSim format, which, as a result, have been used in many recent works. We revisit this conversion tool and find that several key aspects of the CVP-1 traces are not preserved by the conversion. We therefore propose an improved converter that addresses most conversion issues as well as patches known limitations of the CVP-1 traces themselves. We evaluate the impact of our changes on two commits of ChampSim, with one used for the first Instruction Championship Prefetching (IPC-1) in 2020. We find that the performance variation stemming from higher accuracy conversion is significant.</p>
SEM/EDS investigations: traces of wear on the polymers and AISI 4130 rings paired with them
<p>SEM photos and EDS maps/spectra on wear traces of polymer and steel samples for block-on-ring tests presented here: https://doi.org/10.5281/zenodo.10817199</p>
CLDF dataset reflecting Zariquiey, Blum et al.'s "Tracing the Evolution of Panoan Languages in Parallel with Archaeological Changes in the Ucayali Basin", work in progress.
<p>Cite the source of the dataset as:</p> <blockquote> <p>Zariquiey, Roberto and Blum, Frederic and Valenzuela, Pilar and Koile, Ezequiel and Blasi, Damian and Gray, Russell and List, Johann-Mattis. "Tracing the Evolution of Panoan Languages in Parallel with Archaeological Changes in the Ucayali Basin" (work in progress).</p> </blockquote>
A trace of microservice time, call rate, and number of replicas
<p>This is part of the runtime traces of the Alibaba Cloud clusters, which record rows of different microservice times, call rates, and number replicas over a 30-second interval.<br><br>In this dataset, starting from zero, the fifth and sixth columns show the microservice time and call rates (with the microservice ID in column 2 and the container ID in column 3).<br>Moreover, column 4 contains the number of replicas from the microservice, denoted in column 2.</p> <p>Alibaba's original microservice trace has 24 parts, from which we extracted ten and filtered based on message queue and similarity in microservice and container IDs.<br><br>Please consider that the first two columns are timestamps.</p>
Evaluation datasets and results of the paper "Efficient Online Computation of Business Process State From Trace Prefixes via N-Gram Indexing"
<p>Event logs, process models, and results corresponding to the paper "Efficient Online Computation of Business Process State From Trace Prefixes via N-Gram Indexing".</p> <p><em><strong>Inputs</strong></em>: preprocessed event logs and discovered process models (and their characteristics) used in the evaluation.</p> <ul> <li><em><strong>Real-life</strong></em>: preprocessed event logs (<em>xes</em> and <em>csv</em>) corresponding to the real-life processes used in the evaluation. Process models (<em>pnml</em>) discovered with the Inductive Miner infrequent for thresholds of 10%, 20%, and 50%. Characteristics (<em>txt</em>) of the event logs and process models. Ongoing cases result from splitting each case in the preprocessed event logs (under folder <em>split</em>).</li> <li><em><strong>Synthetic</strong></em>: simulated event logs (<em>csv</em>) corresponding to the synthetic processes used in the evaluation. Designed process models (<em>bpmn</em> and <em>pnml</em>). Ongoing cases result from splitting each case in the preprocessed event logs (under folder <em>split</em>). Ongoing cases with injected noise as described in the publication (under folders <em>noise_1</em>, <em>noise_2</em>, and <em>noise_3</em>).</li> </ul>
Dataset for manuscript Tracing Quartz Provenance: A Multi-Disciplinary Investigation of Luminescence Sensitisation Mechanisms of Quartz from Granite Source Rocks and Derived Sediments
<p><span>Quartz optically stimulated luminescence (OSL) sensitivity as well as some electron spin resonance (ESR) and cathodoluminescence (CL) signals have been empirically proposed as provenance indicators. Sensitivity is defined as luminescence emitted in response to a given dose per unit mass. While it is largely believed to be acquired by earth surface processes, recent studies bring evidence that sensitisation processes depend on source geology.</span></p> <p><span>Here we combine OSL and thermoluminescence (TL), ESR and CL analyses to understand the mechanisms of quartz OSL sensitisation. We investigate granites and their derived sediments from catchments draining simple lithologies of known age that display contrasting OSL sensitisation behaviour both in nature and during irradiation and light exposure laboratory experiments. The sample displaying increased OSL sensitisation is characterised by TL emission at intermediate temperatures (150-250 °C), Ti-related signals in CL, and Ti and Ge lithium compensated signals in ESR. <span>The insensitive samples either lack or exhibit very weak such characteristics and contain several times less amount of trace titanium measured by </span></span><span>laser ablation inductively coupled plasma mass spectrometry (</span><span>LA-ICP-MS).</span></p> <p><span>We demonstrate that the OSL sensitisation results as an effect of the existence of certain defects and impurities in the quartz crystal in the parent rock, such as titanium and germanium. However, the degree of sensitisation reached in nature is significantly higher than in the laboratory. <span> </span>As such, the existence of this precursor represents the potential for sensitisation, which can later be amplified by environmental factors during sedimentary history.</span></p>
SCALE-WIN19 Trace Metal Clean Rosette Data
<p>The files here contain the trace metal clean CTD bottle and sensor files for the SCALE Winter Cruise.</p> <p>Oxygen sensor data is now included - except for stations PUZ, SAZ2, GT1, GTE, GT1, MIZ1 and MIZ2.</p> <p>For notes on how the data was processed please refer to the SCALE CTD Processing Report.</p> <p> </p> <table><colgroup><col><col></colgroup> <tbody> <tr> <td><strong>Variable</strong></td> <td><strong>Units</strong></td> </tr> <tr> <td>Temperature</td> <td>degrees C</td> </tr> <tr> <td>Conductivity</td> <td>S/m</td> </tr> <tr> <td>Salinity</td> <td>PSU</td> </tr> <tr> <td>Oxygen in situ/Oxygen theoretical/AOU</td> <td>mL/L</td> </tr> <tr> <td>Oxygen Saturation</td> <td>%</td> </tr> <tr> <td>Density</td> <td>kg/m3</td> </tr> <tr> <td>Chlorophyll</td> <td>mg/m3</td> </tr> <tr> <td>Beam Transmission</td> <td>%</td> </tr> <tr> <td>Beam Attenuation</td> <td>m</td> </tr> </tbody> </table>
SCALE-SPR19 Trace Metal Clean CTD Rosette Data
<p>The files here contain the trace metal clean CTD bottle and sensor files for the SCALE Spring Cruise. </p> <p>Oxygen sensor data is now included.</p> <p>For notes on how the data was processed please refer to the SCALE CTD Processing Report.</p> <table><colgroup><col><col></colgroup> <tbody> <tr> <td><strong>Variable</strong></td> <td><strong>Units</strong></td> </tr> <tr> <td>Temperature</td> <td>degrees C</td> </tr> <tr> <td>Conductivity</td> <td>S/m</td> </tr> <tr> <td>Salinity</td> <td>PSU</td> </tr> <tr> <td>Oxygen in situ/Oxygen theoretical/AOU</td> <td>mL/L</td> </tr> <tr> <td>Oxygen Saturation</td> <td>%</td> </tr> <tr> <td>Density</td> <td>kg/m3</td> </tr> <tr> <td>Chlorophyll</td> <td>mg/m3</td> </tr> <tr> <td>Beam Transmission</td> <td>%</td> </tr> <tr> <td>Beam Attenuation</td> <td>m</td> </tr> </tbody> </table>
The Tracing Convective Momentum Transport in Complex Cloudy Atmospheres Experiment - Level 1
<p>The first field campaign from the Tracing Convective Momentum Transport in Complex Cloudy Atmospheres experiment project (CMTRACE) took place in Cabauw, the Netherlands, between September 13th and October 3rd 2021. During this field campaign, two cloud radars and one wind lidar were operated with a similar scanning strategy for deriving wind speed and direction profiles from near the surface up to cloud tops. Here we provide the daily Level 1 data from each instrument. At this level, several processing steps were applied to the raw data to minimize offsets, reduce the number of spurious data and derive wind speed and direction profiles; however, the data from each instrument is kept on its original spatial and temporal resolution. The raw data is available for the users on request from the corresponding author.</p> <p><strong>Prefix identificaiton:</strong></p> <p>Lidar data: cmtrace_cabauw_wls200-218<br> Scanning radar data: cmtrace_cabauw_rpg_radar_35-94<br> Vertically pointing radar data: cmtrace_cabauw_rpg_radar_94</p> <p> </p>
Elk hair trace minerals and treponeme-associated hoof disease surveillance metadata in the US Pacific Northwest
<p>This is the publicly accessible dataset reporting concentrations of thirteen analyzed minerals from hair using inductively coupled plasma mass spectrometry and relevant metadata from treponeme-associated hoof disease in Pacific Northwest elk. The data presented here were analyzed for the manuscript entitled "Associations between hair trace mineral concentrations and the occurrence of treponeme-associated hoof disease in elk (<em>Cervus canadensis</em>)."</p> <p>Please note that reported mineral concentrations are in their adjusted values and raw forms represented by the column name having ".Raw", (e.g., "Selenium" versus "Selenium.Raw"). Elk ecotype is represented by a four letter abbreviation for either Roosevelt (ROOS) or Rocky Mountain (ROMO). Unknown values for some variables (e.g., age class, county) are denoted with a "U."</p>
Memecry:Tracing the Repetition-with-Variation of Formulas on 4chan/pol/
<p>Datasets underlying the analysis of the paper "Memecry: Tracing the Repetition-with-Variation of Formulas on 4chan/pol/</p> <p>This upload includes the following:</p> <ul> <li><strong>seedwords.csv: </strong>A .csv file with terms we used as a seed list to filter for 4chan/pol/-post containing vernacular.</li> <li><strong>seedword-network_x.gdf/gephi: </strong>.gdf and .gephi network files for NPMI-weighted co-word networks of /pol/-posts. We only included posts that contained one of the aforementioned seed list words.</li> <li><strong>twoflow-data_x.xlsx: </strong>.xlsx files with data on triplets common to 4chan/pol/. We identified these three-word sequences through the above network files. For example: "gr8 b8 m8", "orange man bad", "lurk moar newfag". The Excel data on these triplet includes: <ul> <li>The absolute amount of /pol/-posts per year mentioning the triplets (within a window of five words).</li> <li>The average NPMI scores between the three triplet words per year.</li> <li>The top co-words per year having an average NPMI higher than 0.18 with <em>two of the three</em> triplet words.</li> </ul> </li> <li><strong>triplets.csv</strong>: A .csv file with the extracted triplets, including their common appearance as memetic phrases and a short explanation.</li> </ul> <p>This data was used for "two-flow graphs" available at <a href="http://oilab.eu/formulas/">oilab.eu/formulas/</a>.</p> <p>See the paper for full explanations on the data.</p>
API traces for malware detection
<p>The dataset consists of traces of benign and malware samples. There are approximately 330k traces in the dataset, each meticulously collected and curated for research and analysis purposes, with an uncompressed size of 550GB. The dataset was collected during the second half of 2023. The file "shas_by_families.json" links each SHA(which is also the individual filenames) with the associated malware or benign family. Each file is in json format and includes the parameters of the API call as well.</p> <p> </p> <p>If you are using this dataset, please cite our work on Arxiv.<br>@misc{fellicious2025malwaredetectionbasedapi,<br> title={Malware Detection based on API calls}, <br> author={Christofer Fellicious and Manuel Bischof and Kevin Mayer and Dorian Eikenberg and Stefan Hausotte and Hans P. Reiser and Michael Granitzer},<br> year={2025},<br> eprint={2502.12863},<br> archivePrefix={arXiv},<br> primaryClass={cs.CR},<br> url={https://arxiv.org/abs/2502.12863}, <br>}</p>
Data from: Possible provenance of IRD by tracing late Eocene Antarctic iceberg melting using a high-resolution ocean model
<p>This repository contains the data supplemented to <a href="https://doi.org/10.5194/cp-21-441-2025">Elbertsen et al. (2025)</a> based on Mark Elbertsen's MSc project in which he performed depth-integrated Lagrangian iceberg tracing around Antarctica during the late Eocene using high-resolution ocean model data. Using the OceanParcels framework, iceberg melting (or growth) was simulated using several kernels, including for the dominant iceberg melt terms: basal melt, buoyant convection and wave erosion. By defining kernels for five different order-of-magnitude iceberg size classes, the model was be used to determine the minimum iceberg size required for icebergs to survive the late Eocene warmth. The model output of these simulations can be found here.</p> <p> </p> <p>This research is funded by ERC Starting Grant 802835 (OceaNice) to Peter K. Bijl.</p>
Server-side I/O request arrival traces
<p>Dataset generated for the "<strong>On server-side file access pattern matching</strong>" paper (Boito et al., HPCS 2019).</p> <p>The traces were obtained following the methodology described in the paper. In addition to the two data sets discussed in the paper, we are also making available an extra data set of server traces.</p> <p><strong>Traces from I/O nodes</strong></p> <ul> <li>IOnode_traces/output/commands has the list of commands used to generate them. Each test is identified by a label, and the test_info.csv file contains the mapping of labels to access patterns. Some files include information about experiments with 8 I/O nodes, but these were removed from the data set because they had some errors.</li> <li>IOnode_traces/output contains .map files that detail the mapping of clients to I/O nodes for each experiment, and .out files, which contain the output of the benchmark.</li> <li>IOnode_traces/ contains one folder per experiment. Inside this folder, there is one folder per I/O node, and inside these folders there are tracefiles for the read and write portions of the experiments. Due to a mistake during the integration between IOFSL and AGIOS, read requests appear as "W", and writes as "R". Once accounted for when processing the traces, that has no impact on results.</li> <li>pattern_length.csv contains the average pattern length for each experiment and operation (average number of requests per second), obtained with the get_pattern_length.py script.</li> </ul> <p>Each line of a trace looks like this:</p> <p><code>277004729325 00000000eaffffffffffff1f729db77200000000000000000000000000000000 W 0 262144</code></p> <p>The first number is an internal timestamp in nanoseconds, the second value is the file handle, and the third is the type of the request (inverted, "W" for reads and "R" for writes). The last two numbers give the request offset and size in bytes, respectively.</p> <p><strong>Traces from parallel file sytem data servers</strong></p> <p>These traces are inside the server_traces/ folder. Each experiment has two concurrent applications, "app1" and "app2", and its traces are inside a folder named accordingly:</p> <p><code>NOOP\_app1\_(identification of app1)\_app2\_(identification of app2)\_(repetition)\_pvfstrace/</code></p> <p>Each application is identified by:</p> <p><code>(contig/noncontig)\_(number and size of requests per process)\_(number of processes)\_(number of client machines)\_(nto1/nton regarding the number of files)</code></p> <p>Inside each folder there are eight trace files, two per data server, one for the read portion and another for the write portion. Each line looks like this:</p> <p><code>[D 02:54:58.386900] REQ SCHED SCHEDULING, handle: 5764607523034231596, queue_element: 0x2a11360, type: 0, offset: 458752, len: 32768</code></p> <p>The part between [] is a timestamp, "handle" gives the file handle, "type" is 0 for reads and 1 for writes, "offset" and "len" (length) are in bytes.</p> <ul> <li>server_traces/pattern_length.csv contains the average pattern length for each experiment and operation, obtained with the server_traces/count_pattern_length.py script.</li> </ul> <p><strong>Extra traces from data servers</strong></p> <p>These traces were not used for the paper because we do not have performance measurements for them with different scheduling policies, so it would not be possible to estimate the results of using the pattern matching approach to select scheduling policies. Still, we share them in the extra_server_traces/ folder in the hope they will be useful. They were obtained in the same experimental campaign than the other data server traces, and have the same format. The difference is that these traces are for single-application scenarios.</p>
Generation of synthetic, realistic vehicular traces for three access highways of Quito using SUMO
<p>These files present the maps of three access highways of Quito simulated in SUMO. The contributions are.</p> <ul> <li>Careful validation of the imported maps from OpenStreetMaps (imported in July 2019) including time intervals in traffic lights, location of traffic lights, suppression on non-existing junctions, edges, etc.</li> <li>Simulation of realistic number of vehicles for each road considerings the statistics from traffic authority of Quito</li> <li>Configuration of the 5 generation tools provided in the SUMO package. We used all the meaningful configuration for each tool to obtain synthetic realistic vehicular traces</li> </ul>
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.