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4,763 results for “mobility”
Mobile Cloud Computing Bibliographic Results from Google Scholar
<p>This dataset contains all the results for the term "Mobile Cloud Computing" on Google Scholar until June 2018. The data was acquired using Publish or Perish. The data has been cleaned such that the wrong and invalid results have been removed, duplicates have been removed. Titles are accurate and fine but authors and publishers info. etc. is still unclean. For textual analysis based on paper titles, this dataset is fine. For any other factor, such as institutional or journal or authorship analysis, this isn't a good choice. </p>
Mobile Edge Computing Bibliographic Results from Google Scholar
<p>This dataset contains all the results for the term "Mobile Edge Computing" on Google Scholar until June 2018. The data was acquired using Publish or Perish. The data has been cleaned such that the wrong and invalid results have been removed, duplicates have been removed. Titles are accurate and fine but authors and publishers info. etc. is still unclean. For textual analysis based on paper titles, this dataset is fine. For any other factor, such as institutional or journal or authorship analysis, this isn't a good choice. </p>
Context-Aware 3D Object Anchoring for Mobile Robots Dataset
<p>This dataset accompanies the following publication:</p> <p>Günther, M.; Ruiz-Sarmiento, J. R.; Galindo, C.; González-Jiménez, J. & Hertzberg, J. <strong>Context-Aware 3D Object Anchoring for Mobile Robots.</strong> <em>Robot. Auton. Syst.</em>, 2018 (accepted)</p> <p>The dataset consists of 15 scenes inspected by a robot equipped with a RGB-D camera driving around a table and turning towards it from different locations. The table contained a number of objects in varying table settings. In total, the dataset contains 1387 seconds of observation and 144 unique objects from 9 categories:</p> <ul> <li>SugarPot</li> <li>MilkPot</li> <li>CoffeeJug</li> <li>MobilePhone</li> <li>Mug</li> <li>Dish</li> <li>Fork</li> <li>Knife</li> <li>Spoon</li> <li>TableSign</li> </ul> <p>Segmentation, tracking and local object recognition was run on the recorded sensor data, and its output (tracked objects and local recognition results) was added to the dataset. Since the objects were observed from multiple perspectives and tracking was lost while the robot was moving from one observation pose to another, the dataset contains more than one track ID for most objects (one for each subsequent observation of the object). Each track ID was manually labeled with the ground truth category of the object it represented. Additionally, all track IDs belonging to the same object were manually grouped together to allow evaluation of the anchoring process. Track IDs that did not correspond to any object on the table (but instead to objects on different tables, pieces of the table itself or other artifacts) were manually removed. In total, out of 432 track IDs, 410 (94.9 %) were associated with true objects, while 22 (5.1 %) were removed as artifacts.</p> <p><br> <strong>File contents</strong></p> <p>All data is provided as rosbags. The naming scheme is as follows:</p> <ul> <li>`*-sensordata.bag.bz2`: The raw sensor data from the robot and all transform data, including localization in a map.</li> <li>`*-perception.bag.bz2`: The object recognition results and ground truth information for the tracked objects.</li> <li>`scene??-pr2-*.bag.bz2`: 5 scenes that were recorded using the PR2 robot.</li> <li>`scene??-calvin-*.bag.bz2`: 10 scenes that were recorded using the Calvin robot.</li> </ul> <p>Both robots used an ASUS Xtion Pro Live as 3D camera.</p> <p>`race_vision_msgs.tar.bz2`: The custom messages used in the `-perception` rosbags, as a ROS Kinetic package.</p> <p><br> <strong>Videos</strong></p> <p>To get a first impression of the dataset, `scene10.mp4` and `scene19.mp4` show the corresponding scenes from the point of view of the robot's RGB camera.</p>
Continuous monitoring of patient mobility for 18 months using inertial sensors following traumatic knee injury: a case study
<p><strong>This repository contains raw data relating to: </strong>Continuous monitoring of patient mobility for 18 months using inertial sensors following traumatic knee injury: a case study Mueller A., Hoefling H., Nuritdinow T., et al. DOI: 10.1159/000490919</p> <p><strong>Metadata and processed data derived from the raw data deposited here is available here:</strong> https://github.com/Novartis/mueller_et_al_2018</p> <p><strong>Article Abstract</strong></p> <p>Continuous patient activity monitoring during rehabilitation, enabled by digital technologies, will allow the objective capture of real-world mobility and aligning treatment to each individual’s recovery trajectory in real time. To explore the feasibility and added value of such approaches, we present a case study of a 36-year-old male participant monitored continuously for activity levels and gait parameters using a waist-worn inertial sensor following a tibial plateau fracture on the right side, sustained as a result of a high-energy trauma during a sporting accident. During rehabilitation, data were collected for a period of 553 days, with > 80% daytime compliance, until the participant returned to near full mobility. The participant completed a daily diary with the annotation of major events (falls, near falls, cycling periods, or physiotherapy sessions) and key dates in the patient’s recovery, including medical interventions, transitioning off crutches, and returning to work. We demonstrate the feasibility of collecting, storing, and mining of continuous digital mobility data and show that such data can detect changes in mobility and provide insights into long-term rehabilitation. We make both raw data and annotations available as a resource with the aspiration that further methods and insights will be built on this initial exploration of added value and continue to demonstrate that continuous monitoring can be deployed to aid rehabilitation.</p>
Simulated Self-user Shadowing for Mobile Phone Antennas at 28 GHz and at 60 GHz
<p>The purpose of this dataset is to supplement the data presented in our conference publication "Self-user shadowing effects of millimeter-wave mobile phone antennas in a browsing mode" at EuCAP 2019 (see <a href="https://ieeexplore.ieee.org/document/8739947">https://ieeexplore.ieee.org/document/8739947</a>).</p> <p>This dataset contains the 3-D surface meshes of the two numeric human body models used in the above publication. One body model holds the mobile phone with one hand (vertically, "OneHand") and the other body model with both hands (horizontally, "TwoHand"). The body models were initially exported from MakeHuman (<a href="http://www.makehumancommunity.org">http://www.makehumancommunity.org</a>), the actual body postures were then created with Blender 3D Creation Suite (<a href="https://www.blender.org">https://www.blender.org</a>), and these final body models were exported in OBJ format (a generic geometry definition file format). Then these models were imported into CST Studio Suite (<a href="http://www.cst.com">http://www.cst.com</a>) in order to simulate the 3-D realised-gain patterns of the antenna. The material properties of the human body model used in the simlations are described in detail in the above publication. Also the dual-polarised mobile-phone antenna design with one vertical feed port and one horizontal feed port is described in detail within the above publication (see Fig. 3) and is not part of this dataset. (Note that "port #1" in Fig. 3 of the publication denotes the vertical antenna port for the <em>one-hand</em> case, while "port #1" denotes the horizontally antenna port in the <em>two-hand</em> case.)</p> <p>This dataset also contains the simulated 3-D polarimetric, directional, complex-valued (real, imaginary) realised-gain patterns, seperately for 28 GHz and for 60 GHz, in 1-degree resolution in both phi and theta directions. The patterns are seperately given for the vertical ("VPolPatch")and the horizontal feed port ("HPolPatch"). The 2-D pattern cuts presented in the above publication (in Figs. 5-11) are subsets of the 3-D patterns in this dataset.</p> <p>The format of the eight ascii files {xxGHzStandingyyHandzzPolPatch.txt} is a follows:<br> 1st column: Theta angle in degrees<br> 2nd column: Phi angle in degrees<br> 3rd column: real part of Gain, theta component, in dBi<br> 4th column: imaginary part of Gain, theta component, in dBi<br> 5th column: real part of Gain, phi component, in dBi<br> 6th column: imaginary part of Gain, phi component, in dBi<br> where xx is "28" or "60" (GHz), yy is "One" or "Two" (-hand grip), and zz is "H" or "V" (-pol. antenna port), as described above.</p> <p>The spherical coordinate system is used in accordance to the IEEE-standard spherical coordinate system. The underlying Cartesian coordinate system is shown in the two attached preview (PNG) image files for both human body models, where the z-axis (theta=0 degrees) points to the directions of the head of the human, the x-axis (phi=0 degrees) towards the left side of the human, and the y-axis toward the back of the human.<br> </p>
User stories and xAPI statements for "A mobile campus application as a sensor node for Personal Learning Environments"
<p>This dataset provides the full user stories and xAPI statements as used in the prototype described in the article "A mobile campus application as a sensor node for Personal Learning Environments". It consists of two PDF documents described below. The files were created as part of the master thesis of Hendrik Geßner.</p> <p>"User Stories.pdf" contains a complete list of user stories with required context information, existing portlets and a category. The process that led to this collection is described very briefly in the article mentioned above, a graphical explanation is available in the attached image "Use case process complete.jpg"</p> <p>"xAPI Statements.pdf" contains all xAPI statements used in the prototype described in the article mentioned above. Dynamic elements such as names or IDs are highlighted on color. The statements appear in the following order: attended, used, loggedin, wasat, opened, closed, joined, left.</p>
Context-Aware Dataset: STS - South Tyrol Suggests IoT Mobile App Data
<p><strong>STS dataset </strong>was collected by a context-aware recommender system mobile app named as<strong> <a href="https://play.google.com/store/apps/details?id=it.unibz.sts.android&hl=en">"South Tyrol Suggests"</a></strong>. The app provides <strong>context-aware recommendations</strong> for attractions, events, public services, restaurants, and much more based on the rating preferences and personality factors of users.</p> <p><strong>Contextual</strong> <strong>variables</strong> includes </p> <ul> <li><strong>distance:</strong> far away, near by</li> <li><strong>time available:</strong> half day, one day, more than one day</li> <li><strong>temperature:</strong> burning, hot, warm, cool, cold, freezing</li> <li><strong>crowdedness:</strong> crowded, not crowded, empty</li> <li><strong>knowledge of surroundings:</strong> new to area, returning visitor, citizen of the area</li> <li><strong>season:</strong> spring, summer, autumn, winter</li> <li><strong>budget:</strong> budget traveler, price for quality, high spender</li> <li><strong>daytime:</strong> morning, noon, afternoon, evening, night</li> <li><strong>weather:</strong> clear sky, sunny, cloudy, rainy, thunderstorm, snowing</li> <li><strong>companion:</strong> alone, with friends/colleagues, with family, with girlfriend/boyfriend, with children</li> <li><strong>mood:</strong> happy, sad, active, lazy weekday: weekday, weekend</li> <li><strong>travel goal:</strong> visiting friends, business, religion, health care, social event, education, scenic/landscape, hedonistic/fun, activity/sport</li> <li><strong>means of transport:</strong> no transportation means, a bicycle, a car, public transport</li> </ul> <p>More details can be found here:</p> <p><em>Braunhofer, Matthias, Mehdi Elahi, and Francesco Ricci. <a href="https://www.researchgate.net/profile/Mehdi_Elahi2/publication/283502363_Techniques_for_cold-starting_context-aware_mobile_recommender_systems_for_tourism/links/56ccaa7608ae059e37507cc0.pdf">"<strong>Techniques for cold-starting context-aware mobile recommender systems for tourism</strong>."</a> Intelligenza Artificiale 8, no. 2 (2014): 129-143.</em></p>
AirHeritage Datalake: Multi-site, Multi-season, Multi Unit dataset including Fixed and Mobile Citizen science data from networked Air Quality Low-Cost Multi-Sensors devices and reference stations
<p>This datalake comprises several datasets from <strong>37 networked low cost air quality multisensors</strong> (<strong>30</strong> <strong>mobile</strong> ENEA MONICA(tm) + <strong>7</strong> <strong>fixed</strong>) along with <strong>3</strong> (fixed) + <strong>1</strong> (mobile) <strong>reference stations</strong> operated by Campania Regional Envronmental Protection Agency. The datalake is organized in 3 main directories respectively related to fixed nodes, mobile nodes and nearby reference stations including a mobile laboratory used for colocation campaigns; each subdirectory include its own metadata description file.</p> <p>Data, curated by Energy and Data Science Laboratory of ENEA, include multi-weeks colocation periods when low cost devices have been colocated with reference stations as well as operational periods during which sensors are deployed for fixed or mobile monitoring campaigns. Data have been recorded during 2021 and 2022 in a<strong> pervasive, multi-site, multi-seasonal deployment</strong> in Portici, a densely populated small area city (4km2, 55k + inhabitants) located 7km south of Naples, Italy.</p> <p>The datalake consists in actual sensors and reference intrumentations timeseries along with metadata description files with deployment dates and location data. The dataset files include high sampling frequency raw sensor data of quality-controlled sensor network along with co-located reference stations data sets. Sensor data include electrochemical sensors data (intended target pollutants: NO2, O3, CO), Optical sensor data (PM2.5, PM10, PM1) readings along with meteorological parameters. .</p> <p>Further description of sensors and reference instruments are reported in the accompanying paper (see citation request).</p> <p>The dataset can be used for </p> <ul> <li> <strong>advanced (remote/universal/in field) data driven calibration strategies</strong> test or development including <strong>machine learning </strong>models</li> <li><strong>mobile opportunistic data fusion</strong> methods development</li> <li><strong>geomatics and data assimilation</strong> models studies</li> </ul> <p>as well as low cost sensor characterization performance studies. </p>
Mobile Sleep Lab: Comparison of polysomnographic parameters with a conventional sleep laboratory
<div> <p><span><span> </span></span><span><span>In</span></span> <span><span>remote areas</span></span><span><span>,</span></span><span><span> visiting a laboratory for sleep testing</span><span> is inconvenient</span></span><span><span>.</span></span><span><span> We</span></span><span><span>,</span></span><span><span> therefore</span></span><span><span>,</span></span><span><span> developed a Mobile Sleep Lab in a bus powered by fuel cells with two sleep measurement chambers. As the environment in the bus could affect sleep, we examined whether sleep testing in the Mobile Sleep Lab was as </span><span>feasible</span><span> as in a conventional sleep laboratory (Human Sleep Lab). We tested 15 healthy adults for four nights using polysomnography (</span><span>the </span><span>first two nights at the Human Sleep Lab or Mobile Sleep Lab with a switch to the other facility for the next two nights). Sleep variables of the four measurements were used to assess the </span><span>discrepancy</span><span> of </span><span>different </span><span>place</span><span>s</span><span> or different nights.</span></span> <span><span>N</span><span>o </span><span>significant differences </span><span>were found </span><span>between the laboratories other than the percentage of total sleep time in stage N3. </span><span>Next, we analyzed the intraclass correlation coefficient to evaluate the test-retest reliability. The intraclass correlation coefficient between these two measurements</span></span><span><span>:</span></span><span><span> the Human Sleep Lab and Mobile Sleep Lab showed similar reliability for the same sleep variables. The intraclass correlation coefficient revealed that several sleep indexes, such as </span><span>total sleep time</span><span>, sleep efficiency, wake after sleep onset, percentage of stage N1, and stage R </span><span>latency, </span><span>showed </span><span>poor </span><span>reliabilit</span><span>ies (</span><span><0.5) based on Koo and Li’s criteria</span><span>. In contrast, the percentage of stage N3 showed </span><span>moderate</span></span><span><span> (</span></span><span><span>0.5–0.75</span><span>) or </span><span>good </span></span><span><span>(</span></span><span><span>0.75–0.9</span></span><span><span>) </span></span><span><span>reliabilit</span><span>ies</span></span><span><span>. </span></span><span><span>As </span><span>almost all</span><span> sleep variables </span><span>showed no difference </span><span>and same level of test-retest reliability</span></span> <span><span>between the </span><span>Mobile Sleep Lab </span><span>and </span><span>Human Sleep Lab</span></span><span><span>,</span> </span><span><span>the Mobile Sleep Lab might be suitable</span><span> for conducting polysomnography</span><span> as a conventional sleep laboratory. </span><span>The reduction in N3 in the Mobile Sleep Lab should be scrutinized in the larger sample, including sleep disorders</span></span><span><span>. </span></span><span><span>Practical</span><span> application of the Mobile Sleep Lab can transform sleep medicine in remote areas.</span></span><span> </span></p> </div>
Mobility demand mesh-grid datasets
<ul> <li><strong>15m_flat_bike_count.h5</strong>: Table that contains the number of bike rides that started on every bike station in Chicago, per time interval. Time resolution is 15min, covering 2013-2020 (280512 timestamps).</li> <li><strong>15m_flat_bike_norm_abs.h5</strong>: Same as <strong>15m_flat_bike_count.h5</strong>, but normalized using the maximum number of rides recorded in that period, i.e.: 84. The normalization is calculated as x' = (x - min(X)) / (max(X) - min(X)).</li> <li><strong>15m_flat_taxi_count.h5</strong>: Table that contains the number of taxi trip counts that started on every taxi zone in Chicago, per time interval. Time resolution is 15min, covering 2013-2020 (280512 timestamps).</li> <li><strong>15m_flat_taxi_norm_abs.h5</strong>: Same as <strong>15m_flat_taxi_count.h5</strong>, but normalized using the maximum number of trips recorded in that period, i.e.: 418. The normalization is calculated as x' = (x - min(X)) / (max(X) - min(X)).</li> <li><strong>15m_map_90_60_bike_norm_abs.h5</strong>: Mobility mesh-grid for bikes, calculated from<strong> 15m_flat_bike_norm_abs.h5</strong> counting the total number of rides per element of the grid and time interval. The final shape of the dataset is 280512 x 90 x 60.</li> <li><strong>15m_map_90_60_taxi_norm_abs.h5</strong>: Mobility mesh-grid for taxis, calculated from <strong>15m_flat_taxi_norm_abs.h5</strong> using linear interpolation. The final shape of the dataset is 280512 x 90 x 60.</li> <li><strong>station-locations-bike.csv</strong>: Geolocations of the bicycle racks of Chicago.</li> <li><strong>zone-centroids-taxi.csv</strong>: Geolocations of the centroids of the taxi zones of Chicago.</li> <li><strong>grid-locations-bike.csv</strong>: Locations of the elements of a 90x60 grid overlaying Chicago that contain bicycle racks.</li> <li><strong>holidays.csv</strong>: Holidays in Chicago for the period 2013-2020.</li> </ul>
Data for: Machine learning for predicting environmental mobility based on retention behaviour
<p>This repository contains the data and supplementary information for the paper: "Machine learning for predicting environmental mobility based on retention behaviour".</p>
Accurately Inferring Personality Traits from the Use of Mobile Technology
<p>This dataset contains the features extracted from Spatio-Temporal Mobility and Context of Use and the Big5 scores from the 50-item IPIP survey of 55 volunteers from 6 countries located in 2 continents.</p> <p>The authors predict the Big5 traits by fitting 5 regularized linear regression models, one per trait, and select the regularization parameter and evaluate the prediction performance through nested leave-one-out cross validation.</p> <p><em><strong>Feature extraction pipeline</strong></em></p> <p>For each volunteer, we start the pipeline with 5 time series encoding, in time, her WGS84 coordinates (latitude and longitude), measurements related to her smartphone's battery (charging status and level), surrounding WiFi APs and BT devices, and whether her phone was connected to a WiFi access point.</p> <p>First, we refine the 5 raw time series to accurately describe the spatio-temporal mobility and the context of our volunteers. For example, we create a binary time series that peaks when the user is at home, or when the user is at work, and so on.</p> <p>Next, we process both the refined and the raw time series to extract the features, as follows:</p> <ol> <li><strong>Statistical Features</strong>: We divide the raw time series in intervals of one day. We aggregate the different values within each day into a single numerical measurement (e.g., by computing the average, the count of unique values, the information entropy, or the repetitiveness). Finally, we aggregate the measurements obtained across all days into a single value --- the value of that feature for the selected user --- by measuring the mean (<em>avg</em>), the standard deviation (<em>std</em>), and the coefficient of variation (<em>cov</em>). Features prefixed with <em>avg</em>, <em>std</em>, or <em>cov, </em>have been extracted as described here.</li> <li><strong>Spectral Analysis Features</strong>: We first apply the DFT to the raw time series. Then, we measure: <ol> <li>The frequency of highest energy (we prefix its name with <em>top_frequency</em>);</li> <li>The <em>periodicity</em> of the series in the frequency domain;</li> <li>The energy at the daily and weekly frequencies (<em>daily_energy </em>and <em>weekly_energy</em>);</li> <li>The frequency, the periodicity, and the daily and weekly energy obtained after processing the time series with Welch's method and a two weeks window (<em>w_top_frequency</em>, <em>w_periodicity</em>, <em>w_daily_energy, w_weekly_energy);</em></li> <li>The euclidean distance between the DFT and a pure sine wave with period equivalent to the top frequency of the series (<em>distance_from_sine</em>).</li> </ol> </li> </ol> <p>The string <em>b_day </em>in each name specifies that the features only consider business days (i.e. they exclude holidays and weekends).</p> <p>The 5 columns named O, C, E, A, and N, score the users on the Big5 and represent the prediction targets.</p> <p><em><strong>Source code</strong></em></p> <p>The Python source code developed to engineer and evaluate the embeddings is available <a href="https://www.dropbox.com/s/0nmivoftdfzq4ss/OCEAN_sources.zip?dl=0">here</a>.</p>
Sources and sinks of carbonyl sulfide inferred from tower and mobile atmospheric observations
<p>These datasets include the results of the combination of STILT simulations with COS and CO2 fluxes datasets as well as the observations at the Lutjewad measurement station (LUT, 53.4235°N, 6.3094°E). Please refer to the ReadMe file for further details.</p>
Energy-Saving Strategies for Mobile Web Apps and their Measurement: Results from a Decade of Research - Dataset
<p>In 2022, over half of the web traffic was accessed through mobile devices. By reducing the energy consumption of mobile web apps, we can not only extend the battery life of our devices, but also make a significant contribution to energy conservation efforts. For example, if we could save only 5% of the energy used by web apps, we estimate that it would be enough to shut down one of the nuclear reactors in Fukushima. This paper presents a comprehensive overview of energy-saving experiments and related approaches for mobile web apps, relevant for researchers and practitioners. To achieve this objective, we conducted a systematic literature review and identified 44 primary studies for inclusion. Through the mapping and analysis of scientific papers, this work contributes: (1) an overview of the energy-draining aspects of mobile web apps, (2) a comprehensive description of the methodology used for the energy-saving experiments, and (3) a categorization and synthesis of various energy-saving approaches.</p>
A Gas Chromatography – Ion Mobility Spectrometry dataset for colorectal cancer diagnostic of 56 urine samples corresponding to 29 subjects.
<p><strong>Contents of the dataset</strong></p> <p>The dataset includes the set of urine samples in .mea format, which can be<br> read using the GCIMS R package.</p> <p>It also contains analytical standards in the same format, used for quality<br> control of the equipment and as a retention time alignment reference.</p> <p>If you want to preview the data, you do not need to download the full Urines.zip<br> and AnalyticalStandards.zip files, but rather use the smaller UrinesDemo.zip and<br> AnalyticalStandardsDemo.zip, with a subset of just three samples of the whole<br> dataset.</p> <p>Besides the actual measurements, you will find the annotations.csv and<br> reference_peaks.csv files, with sample annotations and some reference peaks<br> identified in the samples.</p> <p>See further details below.</p> <p><br> <strong>Sample collection</strong></p> <p>Urine samples from 29 subjects were collected at Hospital de Reus. 15 subjects<br> were diagnosed with colorectal cancer, 14 subjects were controls. The study<br> protocol was approved by the Ethics Committee of Hospital de Reus (study<br> approval no. 074/2018).</p> <p>Samples were aliquoted and frozen at -80ºC for storage.</p> <p><strong>Sample preparation</strong><br> </p> <p>Sample preparation improves urine preservation by blocking bacterial growth in<br> the urine, and favours volatile extraction. It also adds an internal standard<br> for verification of instrument variability.</p> <p><em>Stock solution preparation</em></p> <p>Dissolve 11.69 g of NaCl in about 35 mL deionized water and add 6.5 mg sodium<br> azide (NaN3). Once dissolved, add 5.50 mL 5M HCl and mark up to volume with<br> deionized water until the final volume is 50mL. The HCl 5M is used to obtain<br> an acid pH. The pH is controlled with a pH test paper. The final pH level must<br> be 2 or below. The NaCl favors the volatile extraction, and the NaN3 omits<br> the bacterial growth in the urine.</p> <p><em>Internal standard solution preparation</em><br> </p> <p>The 4-flurobenzaldehyde is located in retention time around 200 seconds and<br> can be used as an internal standard.</p> <p>Prepare a methanol stock solution using 100 ml of methanol grade for<br> preparative chromatography and 200 ml of distilled water.</p> <p>Mix 5 mL of 4-fluorobenzaldehyde with 100 mL of the methanol stock solution.</p> <p>Dilute the previous mixture in 400 mL of mili-Q water.</p> <p><br> <em>Sample preparation</em><br> </p> <p>Aliquotes were thawed before analysis. Once thawed, 300uL of the stock solution<br> were added to the urine sample, and 1.5 ml of the acidified urine sample were<br> transferred into a 20ml vial, ensuring only the supernatant of the sample<br> is transferred.</p> <p>Finally, 20 mL of the internal standard solution is added to the sample.</p> <p><strong>GC-IMS Analysis</strong></p> <p>Samples were analyzed with a GC-IMS FlavourSpec® instrument from<br> G.A.S. Dortmund (Dortmund, Germany). Samples were incubated for 15 minutes<br> at 60ºC, the flow rate of the drift gas was set at 200 ml/min, and the carrier<br> gas was set 11 ml/min. Both the drift and carrier gas were Nitrogen 5.0. The GC<br> and IMS temperature were set at 60ºC and the measurement time lasted 33 minutes.</p> <p>Besides the urines, a set of measurements of a ketone mixture was also analyzed<br> at least once per day as an analytical standard control of the equipment. The mixture<br> included 6 ketones (2-butanone, 2-pentanone, 2-hexanone, 2-heptanone,<br> 2-ocatanone and 2-nonanone). This mixture is measured in the same conditions as<br> the urine samples.</p> <p>Samples are provided in the native instrument format (.mea format), that can be<br> read with the GCIMS R package or with the instrument software.</p> <p><strong>Sample annotations</strong></p> <p>The dataset includes a CSV file with sample annotations.</p> <p>The annotations include the following information:</p> <ul> <li>Diagnostic: Either ColorectalCancer or Control</li> <li>Sex: Either Male or Female</li> <li>Sample volume (in ml)</li> <li>Fasting: Whether the sample was collected with the patient in fasting conditions</li> <li>Age in years</li> <li>Weight_kg</li> <li>Height_cm</li> <li>BMI</li> <li>Smoker: TRUE/FALSE, whether the patient smoked</li> <li>Diseases: Whether the patient suffered from ArterialHypertension, CardiacFailure, Cholesterol, Dyslipidemia, Fibromyalgia or Tuberculosis</li> <li>AnalysisDateTime: Date and time of the GC-IMS analysis of the sample</li> </ul> <p><br> <strong>Reference peaks</strong></p> <p>Some peaks were manually annotated to ease the alignment of the samples and explore<br> alignment solutions. While manual peak labelling is not generally required, we<br> attach those reference peaks as well and their locations, in case they are of<br> interest.</p> <p>These reference peaks are found at reference_peaks.csv.</p> <p> </p>
Seasonal controls override forest harvesting effects on the composition of dissolved organic matter mobilized from boreal forest soil organic horizons
<p>Dataset comprised of nutrient fluxes (DOC, TDN, NH4, TDN and SRP), optical parameters related to DOM composition (SUVA, spectral slopes and slope ratio), pH, and other nutrient and elemental ratios for passive pan lysimeters installed across terrestrial sites in Pynn's Brook, Newfoundland.</p>
Mobile Application Privacy Risk Assessments from User-authored Scenarios
<p>Mobile applications (apps) provide users valuable benefits at the risk of exposing users to privacy harms. Improving privacy in mobile apps faces several challenges, in particular, that many apps are developed by low resourced software development teams, such as end-user programmers or in startups. In addition, privacy risks are primarily known to users, which can make it difficult for developers to prioritize privacy for sensitive data. In this paper, we introduce a novel, lightweight method that allows app developers to elicit scenarios and privacy risk scores from users directly using only an app screenshot. The technique relies on named entity recognition (NER) to identify information types in user-authored scenarios, which are then fed in real-time to a privacy risk survey that users complete. The best-performing NER model predicts information types with a weighted average precision of 0.70 and recall of 0.72, after post-processing to remove false positives. The model was trained on a labeled 300-scenario corpus, and evaluated in an end-to-end evaluation using an additional 203 scenarios yielding 2,338 user-provided privacy risk scores. Finally, we discuss how developers can use the risk scores to prioritize, select and apply privacy design strategies in<br> the context of four user-authored scenarios.</p>
Manipulation of photoperiod induces fat storage, but not fat mobilization in the migratory songbird, Dumetella carolinensis (Gray Catbird).
<p>Associated R code and data sets for analyses related to the "Manipulation of photoperiod induces fat storage, but not fat mobilization in the migratory songbird, Dumetella carolinensis (Gray Catbird)." publication. </p>
DMS measurements dataset for manuscript "Classification of Volatile Organic Compounds by Differential Mobility Spectrometry Based on Continuity of Alpha Curves"
<p>Differential mobility spectrometry dispersion plots collected for the manuscrit "Classification of Volatile Organic Compounds by Differential Mobility Spectrometry Based on Continuity of Alpha Curves". The measurement files are located in the folders that represent certain week and day of measurement. The folders containing measurements are named with the following pattern: [chemical abbreviation]_[dilution rate]. For example "2PEtOH_1o10k" means that the folder contains measurement of 2-phenylethanol diluted with propylene glycol in volumetric proportion 1/10 000. Another example is "nBuOH_1o100" - n-Butanol diluted with propylene glycol in volumetric proportion 1/100. Please find the abbreviations in the article referred.</p> <p>For the first five weeks only 1/100 dilutions were measured. The last two weeks (weeks 6 and 7) 1/10 000 dilutions were measured. However, Carvone 1/100 was measured again on week 6 due to suspicion of faulty measurements during the previous weeks. The faulty measurements were not confirmed, and thus there 25 more samples of Carvone with dilution rate 1/100.</p>
Debris Flow Carbon Mobilization Table
This file contains the Tongass National Forest Landslide Inventory ID, planimetric area, aboveground carbon amount, and soil carbon amount mobilized by each landslide. Carbon amounts were determined using two aboveground carbon models and a soil carbon model, available at (https://databasin.org/people/brianbuma/). These values were used to estimate the amount of carbon mobilized by debris flow type landslides in SE Alaska, within the Tongass National Forest.
ScienceDex guides
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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.