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3,481 results for “data set”
Analysis of Data Consistency of Howells' Craniometric Data Sets
<p>Derived data and R scripts for analyzing the data consistency of Howells' craniometric data sets.</p>
data sets from "Updated trends of the stratospheric ozone vertical distribution in the 60S–60N latitude range based on the LOTUS regression model"
<p>Monthly means data sets from satellite, ground-based and model records used in the article entitled: "Updated trends of the stratospheric ozone vertical distribution in the 60 S–60 N latitude range based on the LOTUS regression model"</p> <p> </p>
Probabilistic modeling of the indoor climates of residential buildings using EnergyPlus - data set of indoor temperature and relative humidity
<p>This data supplements the journal article: </p> <p>Buechler E, Pallin S, Boudreaux P, Stockdale M. Probabilistic modeling of the indoor climates of residential buildings using EnergyPlus. <em>Journal of Building Physics</em>. 2017;41(3):225-246. doi:<a href="https://doi.org/10.1177/1744259117701893">10.1177/1744259117701893</a></p> <p>Abstract:</p> <p>The indoor air temperature and relative humidity in residential buildings significantly affect material moisture durability, heating, ventilation, and air-conditioning system performance, and occupant comfort. Therefore, indoor climate data are generally required to define boundary conditions in numerical models that evaluate envelope durability and equipment performance. However, indoor climate data obtained from field studies are influenced by weather, occupant behavior, and internal loads and are generally unrepresentative of the residential building stock. Likewise, whole-building simulation models typically neglect stochastic variables and yield deterministic results that are applicable to only a single home in a specific climate. The purpose of this study was to probabilistically model homes with the simulation engine EnergyPlus to generate indoor climate data that are widely applicable to residential buildings. Monte Carlo methods were used to perform 840,000 simulations on the Oak Ridge National Laboratory supercomputer (Titan) that accounted for stochastic variation in internal loads, air tightness, home size, and thermostat set points. The Effective Moisture Penetration Depth model was used to consider the effects of moisture buffering. The effects of location and building type on indoor climate were analyzed by evaluating six building types and 14 locations across the United States. The average monthly net indoor moisture supply values were calculated for each climate zone, and the distributions of indoor air temperature and relative humidity conditions were compared with ASHRAE 160 and EN 15026 design conditions. The indoor climate data will be incorporated into an online database tool to aid the building community in designing effective heating, ventilation, and air-conditioning systems and moisture durable building envelopes.</p> <p>This supplemental data set includes the hourly temperature and relative humidity for the 10th, 50th, and 90th percentile simulations for each building type in each climate zone. The column headings are of the following format buildingtype_climatezone_output_percentile.</p> <p>There are six building types, B1 (unfinished basement 1-story), B2 (unfinished basement 2-story), C1 (unvented crawlspace 1-story), C2 (unvented crawlspace 2-story), S1 (slab 1-story), and S2 (slab 2-story).</p>
Data set for the replication package of the paper "Simulations of DNA-origami self-assembly reveal design-dependent nucleation barriers"
<p>Data set for the replication package of the paper "Simulations of DNA-origami self-assembly reveal design-dependent nucleation barriers".</p>
data sets from "Updated trends of the stratospheric ozone vertical distribution in the 60S–60N latitude range based on the LOTUS regression model"
<p>Monthly means data sets from satellite, ground-based and model records used in the article entitled: "Updated trends of the stratospheric ozone vertical distribution in the 60 S–60 N latitude range based on the LOTUS regression model".</p> <p>Information about and the most recent versions of each dataset can be found at their individual source locations:</p> <p>Merged satellite datasets</p> <ol> <li>SBUV MOD – https://acd-ext.gsfc.nasa.gov/Data_services/merged/index.html (NASA GSFC, USA)</li> <li>SBUV COH: https://ftp.cpc.ncep.noaa.gov/SBUV_CDR/ (NOAA, USA).</li> <li>GOZCARDS: https://www.earthdata.nasa.gov/esds/competitive-programs/measures/gozcards (JPL, NASA, USA)</li> <li>SWOOSH: https://csl.noaa.gov/groups/csl8/swoosh/ (NOAA, USA).</li> <li>SAGE-CCI-OMPS and MEGRIDOP datasets are available through https://climate.esa.int/en/projects/ozone/data/ and ftp://cci_web@ftp-ae.oma.be/esacci (ESA Climate Office). They are provided by FMI, Finland</li> <li>SAGE-SCIAMACHY-OMPS: data record is available upon registration via the following link: http://www.iup.uni-bremen.de/DataRequest/ (U. Bremen, Germany).</li> <li>SAGE-OSIRIS-OMPS: downloading instructions can be found at https://research-groups.usask.ca/osiris/data-products.php#OSIRISLevel3andMergedDataProducts (U. Saskatchewan, Canada).</li> </ol> <p>Ground-based records:</p> <ol> <li>Umkehr – https://gml.noaa.gov/aftp/data/ozwv/Dobson/AC4/Umkehr/Monthly/ (NOAA, USA)</li> <li>ozonesondes – https://hegiftom.meteo.be/datasets/ozonesondes (HEGIFTOM). Measurements at the various stations are provided by the following institutions: <ul> <li>Hohenpeissenberg: DWD, Germany</li> <li>Payerne:MeteoSwiss, Switzerland</li> <li>OHP, CNRS, France</li> <li>Hilo, NOAA, USA</li> <li>Lauder, NIWA, New Zealand</li> </ul> </li> <li>lidar: <a href="http://www.ndacc.org/">http://www.ndacc.org/</a> . Measurement at the various stations are provided by the following institutions: <ul> <li>Hohenpeissenberg: DWD, Germany</li> <li>OHP: CNRS, France</li> <li>MLO: JPL, NASA, USA</li> <li>Lauder: NIWA, New Zealand</li> </ul> </li> <li>FTIR spectrometers – <a href="http://www.ndacc.org/">http://www.ndacc.org/</a> Three sites only provided quality checked measurements relevant for the article. For other ozone FTIR measurements, data in <a href="http://www.ndacc.org/">http://www.ndacc.org/</a> must be used. Measurement used in the article are provided by the following institutions: <ul> <li>Zugspitze: KIT, Germany</li> <li>Jungfraujoch: ULiège, GIRPAS team, Belgium</li> <li>Lauder: NIWA, New Zealand</li> </ul> </li> <li>Microwave spectrometers: <a href="http://www.ndacc.org/">http://www.ndacc.org/</a> Measurement at the various stations are provided by the following institutions: <ul> <li>Payerne: MeteoSwiss, Switzerland</li> <li>Mauna Loa: NRL, USA</li> <li>Lauder: NRL, USA</li> </ul> </li> </ol> <p>Chemistry Climate Model (CCM) CCMI simulations are avilable at https://blogs.reading.ac.uk/ccmi</p>
Data_set_C2SMART_Project
<p>This dataset can be used to train and test the algorithms proposed in the project. </p>
Data set from 'Sequential Feature Selection for Power System Event Classification Utilizing Wide-Area PMU Data'
<p>The increasing penetration of intermittent, nonsynchronous<br> generation has led to a reduction in total power<br> system inertia. Low inertia systems are more sensitive to sudden<br> changes, and more susceptible to secondary issues that can result<br> in large scale events. Due to the short time frames involved,<br> automatic methods for power system event detection and diagnosis<br> are required. Wide-area monitoring systems can provide<br> the data required to detect and diagnose events; however due to<br> the increasing quantity of data it is next to impossible for power<br> system operators to manually process raw data. The important<br> information is required to be extracted and presented to system<br> operators for real/near-time decision making and control. This<br> paper demonstrates an approach for the wide-area classification<br> of a number of power system events. A mixture of sequential<br> feature selection and linear discriminant analysis is adopted<br> to reduce the dimensionality of PMU data. Successful event<br> classification is obtained by employing quadratic discriminant<br> analysis on wide-area synchronized frequency, phase angle and<br> voltage measurements. The reliability of the proposed method is<br> evaluated using simulated case studies and benchmarked against<br> other classification methods.</p>
Data set for "Room-temperature electrical control of polarization and emission angle in a cavity-integrated 2D pulsed LED"
<p>Data set for the paper "Room-temperature electrical control of polarization and emission angle in a cavity-integrated 2D pulsed LED"</p>
The current and future distribution of the yellow fever mosquito (Aedes aegypti) on Madeira Island [data set].
<p><strong>Additional data for manuscript:</strong> "The current and future distribution of the yellow fever mosquito (<em>Aedes aegypti</em>) on Madeira Island" published in PLOS Neglected Tropical Diseases by José Maurício Santos, César Capinha, Jorge Rocha, Carla Alexandra Sousa.</p> <p><strong>Corresponding authors:</strong> José Maurício Santos (josemauriciosantos@campus.ul.pt) & César Capinha (cesarcapinha@campus.ul.pt).</p> <p> </p> <p> </p> <p> </p>
Data set for "1/f Noise Characterization of Bilayer MoS2 Field-Effect Transistors on Paper with Inkjet-Printed Contacts and hBN Dielectrics"
<p>Data set of the experimental results presented in the article "1/f Noise Characterization of Bilayer MoS2 Field-Effect Transistors on Paper with Inkjet-Printed Contacts and hBN Dielectrics" published in Advanced Electronics Materials.</p>
Data set for "Inkjet-printed low-dimensional materials-based complementary electronic circuits on paper"
<p>Data set of the experimental results presented in the article "Inkjet-printed low-dimensional materials-based complementary electronic circuits on paper" published in npj-2D Materials and applications.</p>
Data: set for "Low-voltage 2D materials-based printed field-effect transistors for integrated digital and analog electronics on paper"
<p>The file reports the raw data of Figure 2, Figure 3 and Figure 4 of the manuscript. Data shown in Figure 2 represent the electrical characterization of the MoS<sub>2</sub> FETs with inkjet-printed silver contacts. Figure 2a is a typical transfer characteristic measured as a function of the gate voltage for a drain voltage of 2.0 V; Figure 2b is a typical output characteristic measured at different gate voltages (from V<sub>GS</sub> = 0.0 V to V<sub>GS</sub> = 1.75 V, steps of 0.25 V). Figure 3 represents the electrical characterization of the MoS<sub>2</sub> FETs with inkjet-printed graphene contacts. In particular, a typical transfer characteristic curve measured as a function of the gate voltage for a drain voltage of 2.5 V is shown and a typical output characteristic curves measured at increasing gate voltages (from V<sub>GS</sub> = 0.0 V to V<sub>GS</sub> = 1.75 V, steps of 0.25 V) are reported in Figure 3b and Figure 3c, respectively. Logic gates and current mirror based on MoS2 FETs with inkjet-printed silver contact are presented in Figure 4. Figure 4c shows the output voltage (left axis) and the voltage gain (right axis) of the inverter gate as a function of the input voltage; Figure 4f the output voltage of the NAND gate as a function of the input states (V<sub>IN1</sub>, V<sub>IN2</sub>). Voltage bias is 5 V for both the inverter and the NAND gate; and Figure 4i g the output current of the current mirror as a function of the output voltage for two different values of the reference current.The file reports the raw data of Figure 2, Figure 3 and Figure 4 of the manuscript. Data shown in Figure 2 represent the electrical characterization of the MoS<sub>2</sub> FETs with inkjet-printed silver contacts. Figure 2a is a typical transfer characteristic measured as a function of the gate voltage for a drain voltage of 2.0 V; Figure 2b is a typical output characteristic measured at different gate voltages (from V<sub>GS</sub> = 0.0 V to V<sub>GS</sub> = 1.75 V, steps of 0.25 V). Figure 3 represents the electrical characterization of the MoS<sub>2</sub> FETs with inkjet-printed graphene contacts. In particular, a typical transfer characteristic curve measured as a function of the gate voltage for a drain voltage of 2.5 V is shown and a typical output characteristic curves measured at increasing gate voltages (from V<sub>GS</sub> = 0.0 V to V<sub>GS</sub> = 1.75 V, steps of 0.25 V) are reported in Figure 3b and Figure 3c, respectively. Logic gates and current mirror based on MoS2 FETs with inkjet-printed silver contact are presented in Figure 4. Figure 4c shows the output voltage (left axis) and the voltage gain (right axis) of the inverter gate as a function of the input voltage; Figure 4f the output voltage of the NAND gate as a function of the input states (V<sub>IN1</sub>, V<sub>IN2</sub>). Voltage bias is 5 V for both the inverter and the NAND gate; and Figure 4i g the output current of the current mirror as a function of the output voltage for two different values of the reference current.</p>
Data set of 1D model runs, CTRL and ICE runs, associated with "Underestimation of oceanic carbon uptake in the Arctic Ocean: Ice melt as predictor of the sea ice carbon pump"
<p>Dataset of one-dimensional runs for investigation on the sea ice carbon pump. Associated with Sect. 3.1 and 4.1 of manuscript "Underestimation of oceanic carbon uptake in the Arctic Ocean: Ice melt as predictor of the sea ice carbon pump".</p>
Quantitative 3D OPT and LSFM datasets of pancreata from mice with streptozotocin-induced diabetes: Sample data sets
<p><span>Mouse models for streptozotocin (STZ) induced diabetes probably represent the most widely used systems for preclinical diabetes research, owing to the compound's toxic effect on pancreatic ß-cells. However, a comprehensive view of pancreatic β-cell mass distribution subject to STZ administration is lacking. Previous assessments have largely relied on the extrapolation of stereological sections, which provide limited 3D-spatial and quantitative information. This data descriptor presents multiple ex vivo tomographic optical image data sets of the full β-cell mass distribution in mice subject to single high and multiple low doses of STZ administration, and in glycaemia recovered mice. The data further include information about structural features, such as individual islet β-cell volumes, spatial coordinates, and shape as well as signal intensities for both insulin and GLUT2. Together, they provide the most comprehensive anatomical record of the effects of STZ administration on the islet of Langerhans in mice. As such, this data descriptor may serve as reference material to facilitate the planning, use and (re)interpretation of this widely used disease model.</span></p>
Data set associated with a research on nephrotoxicity of gasoline fumes in male albino rat: A mechanism-based approach study
<p>Few studies have reported nephrotoxicity of gasoline fumes in animals, but none of the findings has been fully ascertained and the mechanism underlying the observed toxicity still remains unknown. In this study, we present insights into the potential mechanism underlying the nephrotoxicity of gasoline fumes in 72 adult male albino rats. This study showed for the first time that accumulation of gasoline residues and metabolites in the kidney of exposed animal could potentially result in the generation of ROS capable of inducing renal dysfunction. The dataset uploaded are associated with this study. </p>
The OpenMRG data set
<p><strong>Overview</strong></p> <p>Data for the article "OpenMRG: Open data from Microwave links, Radar, and Gauges for rainfall quantification in Gothenburg, Sweden" (Andersson et al. 2022, Earth Syst. Sci. Data, 14, 5411–5426, <a href="https://doi.org/10.5194/essd-14-5411-2022">https://doi.org/10.5194/essd-14-5411-2022</a>)</p> <p>OpenMRG consists of data from 364 bi-directional commercial microwave links (CML, 10s resolution, true coordinates), one C-band radar composite, and 11 rainfall gauges covering Gothenburg, Sweden during June-August 2015. More details on the data structure and files the are provided in readme.txt. </p> <p>We acknowledge the indispensable support for this work by Hi3G Access AB (providing access to the CML network), Göteborgs Stad – Kretslopp och Vatten (providing the City network data), Ericsson AB (gathering CML data), and the Swedish Meteorological and Hydrological Institute (providing the SMHI gauge and radar data). We also thank Lei Bao, Anna Jacobsson, Christina Larsson, Mohamed Mustafa, Mikael Riedel, Johan Selin, Victor Näslund, and Johan Thuresson for valuable technical contributions and discussion.</p> <p>This work was carried out in the context of the projects MEMO (financed by Vinnova, dnr: 2017-03297), FutureCityFlow (financed by Vinnova, dnr: 2019-04701), and Urban skyfallsinformation (financed by the Swedish Ministry of the Environment and Energy, grant 1:10 for climate adaptation).</p> <p><strong>Versions</strong></p> <p>1.1: revised data set pertaining to the ESSD article by Andersson et al. 2022 (<a href="https://doi.org/10.5194/essd-14-5411-2022">https://doi.org/10.5194/essd-14-5411-2022</a>). These revisions were made on recommendation by the reviewers and include changing variable names and adding more variables to the data set. <br> 1.0: Initial data set, used in ESSD preprint.</p>
Classification Data set : Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features
<p>Classification data set (train, validation, test) from the study area based on 27 tiles on the south of the France. Data set are provided for each eco-climatic region. The size corresponds to the data set DS-A. Only one random pixel sampling is provided: seed 0. This data set was used to train Gaussian Processes, Random Forest, Multilayer Perceptron and Lightweight Temporal Self-Attention models.</p> <p>For further details see section VI-A-1 of the pre-print article "Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features ". This article is available <a href="https://hal.archives-ouvertes.fr/hal-03781332">here</a>.</p> <p>The implementation of the models is available in the <a href="https://gitlab.cesbio.omp.eu/belletv/land_cover_southfrance_gp">open source repository</a>.</p>
Data Set "Accurate quantum-chemical fragmentation calculations for ion–water clusters with the density-based many-body expansion"
<p>This data set accompanies the publication "Accurate quantum-chemical fragmentation calculations for ion–water clusters with the density-based many-body expansion"</p> <p>It contains:</p> <p>- xyz files of all considered molecular structures.</p> <p>- PyADF input scripts for running the eb-MBE and db-MBE calculations.</p> <p>- raw results data from the eb-MBE and db-MBE calculations</p> <p>- Jupyter notebooks for generating the plots and tables</p>
Cloud-based User Entity Behavior Analytics Log Data Set
<p>This respository contains the CLUE-LDS (CLoud-based User Entity behavior analytics Log Data Set). The data set contains log events from real users utilizing a cloud storage suitable for User Entity Behavior Analytics (UEBA). Events include logins, file accesses, link shares, config changes, etc. The data set contains around 50 million events generated by more than 5000 distinct users in more than five years (2017-07-07 to 2022-09-29 or 1910 days). The data set is complete except for 109 events missing on 2021-04-22, 2021-08-20, and 2021-09-05 due to database failure. The unpacked file size is around 14.5 GB. A detailed analysis of the data set is provided in [1].</p><p>The logs are provided in JSON format with the following attributes in the first level:</p><ul><li><strong>id</strong>: Unique log line identifier that starts at 1 and increases incrementally, e.g., 1.</li><li><strong>time</strong>: Time stamp of the event in ISO format, e.g., 2021-01-01T00:00:02Z.</li><li><strong>uid</strong>: Unique anonymized identifier for the user generating the event, e.g., old-pink-crane-sharedealer.</li><li><strong>uidType</strong>: Specifier for uid, which is either the user name or IP address for logged out users.</li><li><strong>type</strong>: The action carried out by the user, e.g., file_accessed.</li><li><strong>params</strong>: Additional event parameters (e.g., paths, groups) stored in a nested dictionary.</li><li><strong>isLocalIP</strong>: Optional flag for event origin, which is either internal (true) or external (false).</li><li><strong>role</strong>: Optional user role: consulting, administration, management, sales, technical, or external.</li><li><strong>location</strong>: Optional IP-based geolocation of event origin, including city, country, longitude, latitude, etc.</li></ul><p>In the following data sample, the first object depicts a successful user login (see <i>type: login_successful</i>) and the second object depicts a file access (see <i>type: file_accessed</i>) from a remote location:</p><blockquote><p>{"params": {"user": "intact-gray-marlin-trademarkagent"}, "type": "login_successful", "time": "2019-11-14T11:26:43Z", "uid": "intact-gray-marlin-trademarkagent", "id": 21567530, "uidType": "name"}</p><p> {"isLocalIP": false, "params": {"path": "/proud-copper-orangutan-artexer/doubtful-plum-ptarmigan-merchant/insufficient-amaranth-earthworm-qualitycontroller/curious-silver-galliform-tradingstandards/incredible-indigo-octopus-printfinisher/wicked-bronze-sloth-claimsmanager/frantic-aquamarine-horse-cleric"}, "type": "file_accessed", "time": "2019-11-14T11:26:51Z", "uid": "graceful-olive-spoonbill-careersofficer", "id": 21567531, "location": {"countryCode": "AT", "countryName": "Austria", "region": "4", "city": "Gmunden", "latitude": 47.915, "longitude": 13.7959, "timezone": "Europe/Vienna", "postalCode": "4810", "metroCode": null, "regionName": "Upper Austria", "isInEuropeanUnion": true, "continent": "Europe", "accuracyRadius": 50}, "uidType": "ipaddress"}</p></blockquote><p>The data set was generated at the premises of <a href="https://www.huemer-group.com/">Huemer Group</a>, a midsize IT service provider located in Vienna, Austria. Huemer Group offers a range of Infrastructure-as-a-Service solutions for enterprises, including cloud computing and storage. In particular, their cloud storage solution called <a href="https://www.huemer-group.com/hbox/">hBOX</a> enables customers to upload their data, synchronize them with multiple devices, share files with others, create versions and backups of their documents, collaborate with team members in shared data spaces, and query the stored documents using search terms. The hBOX extends the open-source project <a href="https://nextcloud.com">Nextcloud</a> with interfaces and functionalities tailored to the requirements of customers.</p><p>The data set comprises only normal user behavior, but can be used to evaluate anomaly detection approaches by simulating account hijacking. We provide an implementation for identifying similar users, switching pairs of users to simulate changes of behavior patterns, and a sample detection approach in our <a href="https://github.com/ait-aecid/clue-lds">github repo</a>.</p><p>Acknowledgements: Partially funded by the FFG project DECEPT (873980). The authors thank Walter Huemer, Oskar Kruschitz, Kevin Truckenthanner, and Christian Aigner from Huemer Group for supporting the collection of the data set.</p><p><strong>If you use the dataset, please cite the following publication:</strong></p><p>[1] M. Landauer, F. Skopik, G. Höld, and M. Wurzenberger. <a href="https://doi.org/10.1109/BigData55660.2022.10020672">"A User and Entity Behavior Analytics Log Data Set for Anomaly Detection in Cloud Computing"</a>. <a href="http://bigdataieee.org/BigData2022/"><i>2022 IEEE International Conference on Big Data - 6th International Workshop on Big Data Analytics for Cyber Intelligence and Defense (BDA4CID 2022)</i></a>, December 17-20, 2022, Osaka, Japan. IEEE. [<a href="https://www.skopik.at/ait/2022_bigdata.pdf">PDF</a>]</p>
Visualization of SARS-CoV-2 particles in naso/oropharyngeal swabs by thin section electron microscopy – data set 07
<p>We developed a sedimentation method using desktop ultracentrifugation (see description below) to visualize SARS-CoV-2 particles in suspensions from oro- and/or nasopharyngeal swabs by thin section electron microscopy. A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 07 comprises three stitched image montages recorded from an area of a thin section through the sediment obtained from a swab sample which was negative by quantitative PCR (control). Ciliated cells and extracellular material, such as vesicles and needle-like crystals, are visible, but no coronavirus particles.</p> <p>Related publication: Laue M, Hoffmann T, Michel J, Nitsche A. Visualization of SARS-CoV-2 particles in naso/oropharyngeal swabs by thin section electron microscopy. Virol J. 2023 Feb 6;20(1):21. doi: 10.1186/s12985-023-01981-9. PMID: 36747188; PMCID: PMC9901382.</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.