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1,393 results for “Traces”
Ubuntu One multi-cloud object storage trace sublement to "SkyPIE: A Fast & Accurate Oracle for Object Placement"
<p>These are the workload traces of Ubuntu one used in the evaluation of "SkyPIE: A Fast & Accurate Oracle for Object Placement".</p> <p>These traces derive diverse multi-cloud access pattern on object stores from the trace published in "Dissecting UbuntuOne: Autopsy of a Global-Scale Personal Cloud Back-End." The derivation is described in the SkyPIE paper.</p> <p>The traces are stored in Parquet file format, hence can be read with a Parquet reader such as the one included in Pandas. The file names specify the number of regions issuing accesses and the percentage of accesses to object that originate from regions other than the home region, see the publication.</p>
Data accompanying the manuscript "Biogeochemical cycling of trace elements and nutrients in ferruginous waters – constraints from a deep oligotrophic ancient lake", published in Limnology and Oceanography (doi: 10.1002/lno.12687)
<p>CTD and geochemical data accompanying the publication: Biogeochemical cycling of trace elements and nutrients in ferruginous waters – constraints from a deep oligotrophic ancient lake in Limnology & Oceanography (doi: 10.1002/lno.12687).</p>
Reproduction package for: 'Exploring Waveform Variations among Neutron Star Ray-tracing Codes for Complex Emission Geometries'
<p>Data files, python scripts and notebooks to reproduce the code output comparisons performed in "Exploring Waveform Variations among Neutron Star Ray-tracing Codes for Complex Emission Geometries" by Choudhury et al. (2024; <a href="https://doi.org/10.3847/1538-4357/ad7255" target="_blank" rel="noopener"><em>ApJ</em> <strong>975</strong> 202</a>, <a href="https://doi.org/10.48550/arXiv.2406.07285" target="_blank" rel="noopener">arXiv.2406.07285</a>).</p> <p>Please refer to the README for detailed information.</p> <p>N.B. The neutral hydrogen column density (${\rm N}_{\rm H}$) value is mentioned in the paper to be $0.2 \times 10^{20} {\rm cm}^{-2}$, whereas all the analyses in the paper, as reflected in this Zenodo package, actually uses ${\rm N}_{\rm H} = 2 \times 10^{20} {\rm cm}^{-2}$.</p>
Open-Access Data for "Received SignalStrength Measurements with BLE Signals for Contact Tracing and Proximity Detection"
<p>This archive contains three folders which are supplementary material for the paper accepted for publishing in IEEE Sensors Journal.</p> <p><strong>Contents:</strong></p> <ul> <li> The folder `open-access-data/upb/` contains the measurements acquired at UPB. The subfolders are named as `upb_ble_*`, where an asterisk masks the directory number. Whenever UPB is specified, use the data sets from the corresponding directory.</li> <li>The folder `open-access-data/tau/` contains the measurements acquired at TAU. The subfolders are named as `tau_ble_*`, where an asterisk masks the directory number. Whenever TAU is specified, use the data sets from the corresponding directory.</li> <li>The folder `open-access-data/wifi-on-off/` contains a sample code to read the files and plot the data from Fig. 14 in `open-access-data/wifi-on-off/wifi_on_off_read_plot.py` and Fig. 15 in `open-access-data/wifi-on-off/wifi_on_off_read_plot.ipynb`.</li> </ul> <p><strong>Results based on the data have been presented in the paper:</strong><br> Flueratoru, L., Shubina, V., Niculescu, D., Lohan, E.S. (2021). On the High Fluctuations of Received Signal Strength Measurements with BLE Signals for Contact Tracing and Proximity Detection, IEEE Sensors, Special Issue on Advanced Sensors and Sensing Technologies for Indoor Positioning and Navigation</p> <p><strong>To cite these data sets please use the following:</strong><br> Laura Flueratoru, Viktoriia Shubina, Dragoș Niculescu, & Elena Simona Lohan. (2021). Open Access Data for "Received SignalStrength Measurements with BLE Signals for Contact Tracing and Proximity Detection" [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4643668</p>
Tracing and visualisation of contributing water sources in a model of flood inundation: video supplement
<p>These are video supplement files to Wilson & Coulthard (2021), produced using version 1.8f-WS of CAESAR-Lisflood software, <a href="https://doi.org/10.5281/zenodo.5541122">available on Zenodo here</a>. For a full description of the methodology and case studies, please refer to the paper which is available here: <a href="https://doi.org/10.5194/gmd-2021-340">https://doi.org/10.5194/gmd-2021-340</a>.</p> <p>Video animations (no audio) for the following case studies are included:</p> <p>1. <strong>Carlisle, United Kingdom</strong> (carlisleanimation-sourcetracing.avi and carlisleanimation-depthonly.avi):</p> <ul> <li>Simulation of the January 2005 flood event at the confluence of the Rivers Caldew, Petteril and Eden, using a 5 m grid.</li> <li>Both water source tracing and depth only versions are provided.</li> <li>In the water tracing version, blue colours represent flows from the River Eden, reds are from the River Petteril and greens are from the River Caldew; darker shades represent deeper water. Available on YouTube here: <a href="https://youtu.be/xOtOi06cXvA">https://youtu.be/xOtOi06cXvA</a></li> <li>In the depth only version, darker shades of blue represent deeper water, with no information about the water source in a grid cell. Available on YouTube here: <a href="https://youtu.be/aFz-sPRGHVE">https://youtu.be/aFz-sPRGHVE</a></li> </ul> <p>2. <strong>Avon-Heathcote estuary in Christchurch, New Zealand</strong> (avonheathcoteanimation.avi):</p> <ul> <li>Simulation for July 2017, which included a high flow event on 22 July, using a model grid of 10 m.</li> <li>Blue colours represent flows from tide, reds are from the River Avon and greens are from the Heathcote River; darker shades represent deeper water.</li> <li>Available on YouTube here: <a href="https://youtu.be/Fczr5tczzXU">https://youtu.be/Fczr5tczzXU</a></li> </ul> <p>3. <strong>Amazon </strong>(amazonanimation.avi):</p> <ul> <li>Simulation at the confluence of the Solimões (mainstem Amazon) and Purus rivers in the central Amazon, Brazil, for the period of 1 October 2013 through December 2014, using a ~270 m model grid.</li> <li>Red colours are from the Solimões, green colours are from the Purus; darker shades represent deeper water.</li> <li>Available on YouTube here: <a href="https://youtu.be/PknAL_8fd1I">https://youtu.be/PknAL_8fd1I</a></li> </ul> <p>4. <strong>Planar slope</strong> (planaranimation.avi):</p> <ul> <li>A simple test case consisting of a 2000 x 1000 m planar slope (0.001 m/m), with walls added at 250 m intervals across the slope, each of which has several gaps through which water can flow. Model grid was 5 m.</li> <li>Eight water sources were traced in total, with three visualised in the animation: red = source 2, green = source 4, blue = source 6. Depths are shown in the middle plot.</li> <li>Available on YouTube here: <a href="https://youtu.be/DTw8ysJtx8o">https://youtu.be/DTw8ysJtx8o</a></li> </ul> <p>Please feel free to use these animations, under the terms of the CC-BY-4.0 license. Please provide a link back to this site and a citation to Wilson & Coulthard (2021).</p> <p>Reference:</p> <p>Wilson, M. D. and Coulthard, T. J.: Tracing and visualisation of contributing water sources in the LISFLOOD-FP model of flood inundation, Geosci. Model Dev. Discuss. [preprint], <a href="https://doi.org/10.5194/gmd-2021-340">https://doi.org/10.5194/gmd-2021-340</a>, in review, 2021</p>
Edge infrastructure traces
<p>These Edge infrastructure traces consist of bandwidth and latency between devices along with the execution time of the microservices of a video processing application on the different devices. These traces were collected between 2022-10-24 and 2022-11-09.</p> <p>The execution times are related to a set of microservices, including video encoding and framing, along with the training and inference model for road sign classification.<br>The microservices' descriptions are provided in the following research paper:<br>N. Mehran, Z. N. Samani, D. Kimovski, and R. Prodan, "Matching-based Scheduling of Asynchronous Data Processing Workflows on the Computing Continuum," 2022 IEEE International Conference on Cluster Computing (CLUSTER), 2022, pp. 58-70, DOI: 10.1109/CLUSTER51413.2022.00021.</p> <p><br>Devices are as follows:<br>- D01 machine has a twelve-core AMD Ryzen Threadripper 2920X processor with 32GB memory;<br>- D02 machine has an eight-core Intel Core(TM) i7-7700 processor with 16GB memory;<br>- Nvidia Jetson Nano machine has a four-core ARM Cortex-A72 processor with 4GB memory;<br>- Raspberry Pi 4 has a four-core ARM Cortex A57 processor with 4GB of memory.</p> <p>Moreover, the dataset provides the network traces regarding the bandwidth and latency between the Edge devices. We provided the size of the transmitted messages between the devices for the throughput measurements. For the network latency, the information related to the round trip time is calculated by ICMP message request and reply.</p> <p>The traces include five parts of data as follows:</p> <p>BW-MessageSize.csv<br>- Timestamp: Date and time in CET<br>- Source device: the throughput from which we are checking; D02 machine<br>- Destination device: the throughput to which we are checking; D01 or D02 machine<br>- Message size (MB): the message size transmitted between the devices<br>- BW (Mbps): the maximum achievable bandwidth<br>*** "-1" means "iperf3: error - unable to connect to server: Connection refused"</p> <p> </p> <p>JetsonNano-ExecutionTime.csv<br>- Timestamp: Date and time in CET<br>- Device: the Nvidia Jetson Nano single-board computer<br>- Microservice: includes encoding, framing, training, or inference of Dockerized microservices<br>- Execution Time (seconds)</p> <p><br>Large-ExecutionTime.csv<br>- Timestamp: Date and time in CET<br>- Device: the highest performance machine (D01 machine) in the C3 testbed<br>- Microservice: includes encoding, framing, training, or inference (containers)<br>- Execution time (seconds)</p> <p> </p> <p><br>Latency.csv<br>- Timestamp: Date and time in CET<br>- Source device: the latency from which we are checking; D02 machine<br>- Destination device: the latency to which we were checking; D01 or D02 machine<br>- Minimum latency (ms) of the round-trip times among four transmitted messages<br>- Average latency (ms) of the round-trip times among four transmitted messages<br>- Maximum latency (ms) of the round-trip times among four transmitted messages<br>- Mean standard deviation of the round-trip times among four transmitted messages</p> <p> </p> <p><br>RPi4-ExecutionTime.csv<br>- Timestamp: Date and time in CET<br>- Device: Raspberry Pi v4 single-board computer<br>- Microservice: includes encoding, framing, training, or inference (containers)<br>- Execution time (seconds)</p> <p> </p> <p>For more information regarding the testbed, please refer to the C3 website at https://c3.itec.aau.at/.</p> <p><br>Authors:<br>Zahra Najafabadi Samani, Narges Mehran, Dragi Kimovski, Josef Hammer, Radu Prodan<br>Institute of Information Technology, Alpen-Adria-Universitaet Klagenfurt, Austria</p> <p><br> </p>
Laboratory toxicity incubation experiments on phytoplankton using trace metals (Cu, Cd, Zn)
<p>This data compilation contains previously published toxicity threshold concentrations of copper, cadmium and zinc for different phytoplankton, as determined by incubation experiments. The data was recalculated to nmol/L for consistency, assuming the following molar masses of copper, cadmium and zinc as 63.546, 112.411 and 65.380 g/mol, respectively, and salinity as 1.025 kg/L. The growth medium is included in the dataset, as well as the environment where the phytoplankton in question may commonly occur (open or coastal ocean). </p>
Potentially toxic trace metal (Cu, Cd) threshold concentrations for phytoplankton at given open and coastal locations
<p>This data compilation contains previously published threshold concentrations of copper and cadmium of phytoplankton in open and coastal oceans. The data was recalculated to nmol/L for consistency, assuming the following molar masses of copper and cadmium as 63.546 and 112.411, respectively, and salinity as 1.025 kg/L. The temperature and salinity provided by the authors were also included, in case there is a desire for future users to utilise different conversion methods to recalculate original data. Only data with information on whether the authors measured the trace metal concentrations in open or coastal marine environments were included, along with the name of the phytoplankton. The oceans were divided into geographical sections, namely the Atlantic Ocean, Indian Ocean, Pacific Ocean and Southern Ocean, and subsequently further subdivided according to the information authors have given in their publications. In this context, several chemically diverse seas were included in geographical regions in order to limit the number of broad ocean regimes. Chemically diverse sub-regimens were, however, considered within each geographical grouping.</p>
Concentrations of trace metals (Cu, Cd, Zn) in the ocean at given open and coastal locations
<p>This data compilation contains previously published concentrations of copper, cadmium and zinc in open and coastal oceans of the world. The data was recalculated to nmol/L for consistency, assuming the following molar masses of copper, cadmium and zinc as 63.546, 112.411 and 65.380 g/mol, respectively, and salinity as 1.025 kg/L. The temperature and salinity provided by the authors were also included, in case there is a desire for future users to utilize different conversion methods to recalculate original data. Only data with information on whether the authors measured the trace metal concentrations in open or coastal marine environments were included. The oceans were divided into different geographical regions, namely the Atlantic Ocean, Pacific Ocean, Indian Ocean and Southern Ocean, and subsequently subdivided according to the information authors have given in their publications. In this context, several chemically diverse seas were included in geographical regions in order to limit the number of broad ocean regimes. Chemically diverse sub-regimens were, however, considered within each geographical grouping.</p>
Real Power Consumption Traces
<p>We collected 5 real power consumption traces and we used one to generate the training dataset for the LSTM. The other four real power consumption traces were used for the testing dataset.</p> <p>Each real power consumption trace consists of a pair of lists:</p> <ul> <li>traces: a list of samples, which are the measurements of power consumption</li> <li>cycle: a list of element pairs, where each pair consists of: <ul> <li>a numeric tag identifying the field operation performed (Addition, Subtraction, Multiplication, Square and other routine operations not of interest to us):</li> <li>the clock ticks counter when the field operation starts</li> </ul> </li> </ul>
Traces for studying Datacenter Scheduler Programming Abstractions
<p>Traces for the experiments for the research work that investigates the performance impact of various datacenter scheduler programming abstractions.</p>
Observations of trace gases in the lowermost stratosphere and upper troposphere from the SPURT aircraft measurement program
<p><strong>Introduction</strong></p> <p>SPURT (Spurenstofftransport in der Tropopausenregion, trace gas transport in the tropopause region) was an aircraft measurement program funded by the AFO 2000 programme of the German Ministry for Education and Research (BMBF). Eight campaigns (36 flights in total) were conducted between November 2001 and July 2003 to investigate trace gas transport in the extratropical upper troposphere and lowermost stratosphere in all seasons. A wide range of trace gases with different lifetimes and sink/source characteristics were measured in-situ from a Learjet 35A aircraft flying at altitudes up to 13.7 km. The data set is well suited for studies of atmospheric transport, for model validation, and for investigations of seasonal changes in the upper troposphere and lowermost stratosphere as demonstrated in numerous accompanying studies.</p> <p><strong>Dataset content</strong></p> <ol> <li>In-situ measurements of N<sub>2</sub>O, CH<sub>4</sub>, CO, CO<sub>2</sub>, CFC12, H<sub>2</sub>, SF<sub>6</sub>, NO, NO<sub>y</sub>, O<sub>3</sub> and H<sub>2</sub>O along all flight tracks.</li> <li>Position and meteorological quantities recorded by the aircraft along all flight tracks.</li> <li>Meteorological data from ECMWF analysis fields and derived products such as potential vorticity and equivalent latitude interpolated to all flight tracks.</li> <li>Merge files (extension .mrg) of all observations, aircraft positions and other data merged into one single file per flight at 5 sec temporal resolution. Due to different instrument response times or computer clocks, the individual measurements were typically shifted by several seconds relative to each other. These time shifts are corrected for in the merge files.</li> <li>Ten day backward trajectories started every 12 minutes along the flight tracks computed with <em>Lagranto</em> (<a href="https://dx.doi.org/10.5194/gmd-8-2569-2015">doi:10.5194/gmd-8-2569-2015</a>) based on 3-hourly ECMWF IFS analysis/forecast fields.</li> <li>Further information such as flight quicklooks, flight protocols, meteorological reports, etc.</li> </ol> <p>All measurement data are provided in NASA/Ames format (<a href="https://espo.nasa.gov/content/Ames_Format_Specification_v20">https://espo.nasa.gov/content/Ames_Format_Specification_v20</a>), which is a self-explaining ASCII format that can conveniently be read by many software packages, e.g. the nappy library for python.</p> <p><strong>Quick start guide</strong></p> <ol> <li>Download and unpack the gzip compressed tar file (unpacking generates the two directories <em>images </em>and <em>data</em>)</li> <li>Change to the directory <em>data </em>and open the file index.html with a web browser. This will open a web page providing an overview of the eight campaigns and associated data.</li> <li>For most purposes it will be sufficient to work with the merge files: Change to the <em>data</em> directory and list all merge files by typing "ls */*/*.mrg" (Linux) or "dir *\*\*.mrg" (Windows).</li> </ol> <p> </p> <p><strong>References:</strong></p> <p>The reference journal article for the SPURT project is</p> <p><em>Engel, A., Bönisch, H., Brunner, D., Fischer, H., Franke, H., Günther, G., Gurk, C., Hegglin, M., Hoor, P., Königstedt, R., Krebsbach, M., Maser, R., Parchatka, U., Peter, T., Schell, D., Schiller, C., Schmidt, U., Spelten, N., Szabo, T., Weers, U., Wernli, H., Wetter, T., and Wirth, V.: Highly resolved observations of trace gases in the lowermost stratosphere and upper troposphere from the Spurt project: an overview, Atmos. Chem. Phys., 6, 283–301, https://doi.org/10.5194/acp-6-283-2006, 2006. </em></p> <p>Many more scientific publications emerged from the project (see reference list and object identifiers).</p>
Pyrite Trace Element Dataset
<p>The dataset used in <em><strong>Interpreting Mineral Genesis Classification with Decision Maps: A Case Study Using Pyrite Trace Elements.</strong> </em></p>
Data Artifact: Rebasing Microarchitectural Research with Industry Traces
<p>Data Artifact of the paper "Rebasing Microarchitectural Research with Industry Traces", published at the 2023 IEEE International Symposium on Workload Characterization. It includes the original CVP-1 traces used in the paper.</p><p><i>Note</i>: the improved converted traces used in the paper are available at https://doi.org/10.5281/zenodo.10199624.</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>
Trace metal, ion, and nutrient concentrations in aeolian samples subjected to experimental freeze-thaw cycles, collected from Taylor Valley, McMurdo Dry Valleys, Antarctica (2013-2016)
This data package contains measurements of trace metal, ion, and nutrient concentrations in aeolian samples collected from several locations throughout Taylor Valley in the McMurdo Dry Valleys of Antarctica during the 2013-2014, 2014-2015, and 2015-2016 austral summers. Samples were collected by the McMurdo Dry Valleys Long Term Ecological Research Program (MCM LTER) using Big Spring Number Eight (BSNE) isokinetic wind samplers located at Explorer’s Cove, Lake Fryxell at F6, East Lake Bonney, and Taylor Glacier. Samples were then subjected to experimental freeze-thaw cycles in a controlled laboratory setting to simulate supraglacial weathering processes and then analyzed to understand how freeze-thaw cycles affect nutrient, ion, and trace metal concentrations over time.
Correcting False Memories: The Effect of Mnemonic Generalization on Original Memory Traces
Open the record for dataset details and reuse information.
Major and trace bulk sample and micro-XRF geochemistry, carbon and oxygen stable isotope compositions of magmatic and sedimentary rocks from Hovedøya Island, Oslo fjord, Norway.
<p>This data set reports on the methodologies and results of geochemical analysis carried out on samples of magmatic rock, calcite and sedimentary rocks of Hovedoya Island, Oslo fjord, Norway, in the framework of the publication by Poppe et al. (2020; <em>Geochemistry, Geophysics, Geosystems</em>; <a href="https://doi.org/10.1029/2019GC008685">https://doi.org/10.1029/2019GC008685</a>). The major and trace element bulk sample geochemical analysis was carried at the Laboratoire G-Time, Université Libre de Bruxelles, Brussels (V. Debaille), the micro-XRF mapping and line scanning, was carried out at the laboratory of the Analytical and Environmental Geo-Chemistry (AMGC) group at the Vrije Universiteit Brussel (VUB), Brussels (N.J. de Winter, S. Poppe) and the stable isotope composition analysis was carried out as well at the AMGC laboratory (S. Poppe, S. Goderis), supervised by P. Claeys and M. Kervyn, in collaboration with P. Boulvias. Data sheets are provided in .csv or .xlsx format and compressed folders containing .TIF images of µXRF elemental maps are attached. This data set also contains the complete data sets obtained for the construction of calibration curves for µXRF line scan analysis of rock samples of magmatic composition at the AMGC laboratory at VUB.</p>
Execution Traces of an MNIST Workflow on a Serverless Edge Testbed
<p>For the evaluation of a Serverless Edge Computing platform, we built an Edge Cloud testbed consisting of several machines:</p> <ul> <li>A Cloud VM</li> <li>An Nvidia Jetson TX</li> <li>Four Raspberry Pi 3b+</li> <li>Two Intel NUCs with i5 processors</li> </ul> <p>We were interested in profiling these devices with a Machine Learning workflow deployed as a serverless application. To that end, we implemented three functions: Preprocessing, Training, and Serving as OpenFaaS functions. The workflow trains an MNIST model.</p> <p> </p>
Fig. 3. Leaf tracings. A. Group 3 in A revision and one new species of Begonia L. (Begoniaceae, Cucurbitales) in Northeast India
Fig. 3. Leaf tracings. A. Group 3: lanceolate, dentate and very asymmetric leaves. B. Group 4: linear to lanceolate, and entire to denticulate leaves.
Synthetic, realistic vehicular traces for financial district of Quito using SUMO
<p>These files present the map of the financial district of Quito simulated in SUMO. The contributions are.</p> <ul> <li>Careful validation of the imported maps from OpenStreetMaps (imported in November 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 considering the statistics from traffic authority of Quito</li> <li>Configuration of the generation tools provided in the SUMO package. We used all the meaningful configuration for each tool to obtain synthetic realistic vehicular trace</li> </ul> <p>In addition, we include the scripts and program to analyze vehicular traces from the point of view of vehicular communications. The scripts were developed using awk, bash and R. The software to compute connectivity matrix was developed with C++</p>
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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.