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8,020 results for “pilot”
Basic Needs and Student Success Survey (Pilot 2)
<p>Food insecurity among college students is a serious problem that can impact student performance in the classroom and ultimately effect student success. The <a href="https://www.csuchico.edu/chc/">Center for Healthy Communities</a> (CHC) developed the <strong>Basic Needs Student Success Survey (BNS3)</strong> and administered it to undergraduate students participating in the <a href="https://www2.calstate.edu/attend/student-services/eop">Educational Opportunity Program (EOP)</a> at three California State Universities between November 2020 and March 2021. </p> <p>The purpose of this second cross-sectional pilot study was to revise the BNS3 tool and validate student perception of the following:</p> <ol> <li>The impact of receiving <a href="https://www.csuchico.edu/calfresh/index.shtml">CalFresh</a> assistance.</li> <li>The impact of utilization of the campus food pantry on their health, nutrition, cooking confidence, time management and academic performance.</li> </ol> <p>This entry contains </p> <ol> <li>The anonymized and cleaned data set</li> <li>A codebook (data dictionary)</li> <li>The survey tool as a Qualtrics export to Word file</li> </ol>
Water analysis dataset > Water Sentinels pilot project - ACTION project
<p><a href="http://www.ocean-alive.org/en/water-sentinels">WATER SENTINELS</a> is a citizen-science pilot by Ocean Alive to promote water quality at Sado estuary.</p> <p>This dataset presents data and metadata for water quality assessment on water samples collected at Sado estuary by citizens during the 6-month pilot. The samples were analyzed by students and researchers at Setúbal School of Technology (Polytechnic Institute of Setúbal). </p> <p>The data collected by citizens at the field was location (GPS coordinates), temperature and water transparency (using the secchi disc method). In the laboratory researchers evaluated pH, salinity, conductivity, and concentration of ammonia, nitrate, phosphate, suspended solids (SST) and organic matter (SOM and DOM).</p> <p>WATER SENTINELS is one of the pilots supported by <a href="https://actionproject.eu/water-sentinels/"><strong>ACTION</strong></a> project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement number 824603.</p>
Data for Project 'Feasibility, Usability and Acceptance of a Newly Developed Exergame-Based Training Concept for Older Adults with Mild Neurocognitive Disorder - A Pilot Randomized Controlled Trial'
<p>Data for Project 'Feasibility, Usability and Acceptance of a Newly Developed Exergame-Based Training Concept for Older Adults with Mild Neurocognitive Disorder - A Pilot Randomized Controlled Trial' (trial registered at clinicaltrials.gov (<a href="https://clinicaltrials.gov/ct2/show/NCT04996654">NCT04996654</a>; date of registration: 11 July 2021), consisting of:</p> <p>(1) the original and complete data set for all primary outcomes ('Data_Primary-Outcomes_Brain-IT-Pilot-Feasibility-RCT_for-publication.xlsx');</p> <p>(2) the original and complete data set for all secondary outcomes ('Data_Secondary-Outcomes_Brain-IT-Pilot-Feasibility-RCT_for-publication.xlsx');</p> <p>(3) the original and complete data set for all other outcomes (i.e. baseline factors (demographic data, type of usual care interventions) and training heart rate; 'Data_Other-Outcomes_Brain-IT-Pilot-Feasibility-RCT_for-publication.xlsx');</p> <p>(4) folder including the raw and processed heart rate variability (HRV) and electroencephalography (EEG) data for all participants and measurements (HRV-and-EEG_raw-and-processed-data.zip);</p> <p>(5) a corresponding README file including (a) general information, (b) data and file overview, (c) sharing and access information, (d) methodological information, and (e) data-specific information.</p>
EIT-EIC COLLAB PILOT
<p>In the framework of this project we have collected 162 applications from different start-ups across Europe that are looking for our support and aim to join and participate in a selected set of our programmes. From them, 40 start-ups have been selected.</p>
PAsCAL WP6 Pilot 2 Autonomous Driving Training
<p>This dataset was collected within the context of the PAsCAL research project between January 2022 and February 2022 at the ACI Vallelunga test circuit and premises in Rome, Italy. Subject of the pilot was a driving training for advanced ADAS systems and test driving of a Level-2+ autonomous vehicle on a test track, performing several different manoeuvres to test the capability of the ADAS systems.</p> <p>Some of the participants were subjected to a driving training for autonomous vehicles before they did the test drive, wherein they had to perform several difficult driving manoeuvres (such as on slippery ground or emergency braking). The purpose of this pilot was to observe whether a driving training improves the driver's capability to use ADAS systems and therefore operate the vehicle in a safer way. Depending on the pilot, it is recommended to adapt existing driving training for beginners, professionals and experienced drivers.</p> <p>In order to analyse the answers given to the questions, it is recommended to consult also the "PAsCAL WP6 Pilots Surveys" dataset, which contains all questions and possible answers.</p>
PAsCAL WP6 Pilot 4 Shared Connected Transport
<p>These two datasets were collected within the context of the PAsCAL research project between November 2021 and June 2022. Each of the surveys were dedicated to two different pilot scenarios, settings and vehicles:</p> <ol> <li>Shared Connected Vehicle Fleet: An existing rental service for a vehicle fleet for employees and students of the University of Luxembourg was enhanced by adding an advances Level-2+ vehicle to the fleet. The users were already familiar with the functionality of the service (booking process, etc.) and were asked to take a realistic trip including urban areas but also a strip of highway and were invited to test the autonomous features of the vehicle (removing hands from steering wheel, automatic parking and many more). The pilot took place in the Belval area of Luxembourg;</li> <li>Bus shuttle: An autonomous bus shuttle with Level-4 autonomy was piloted, which connects a train station to a business park. Participants were workers or visitors of the business park and the objective of this pilot was to observe the adequacy of the shuttle in the commuting context.</li> </ol> <p>In order to analyse the answers given to the questions, it is recommended to consult also the "PAsCAL WP6 Pilots Surveys" dataset, which contains all questions and possible answers.</p>
Automated monitoring of biodiversity in the tropics: A pilot study at Barro Colorado Island
<p>Automated monitoring of biodiversity using camera systems and acoustic devises is becoming increasingly common practise. The development of camera traps for insects, however, is relatively new, and has not been tested in tropical environments. To understand both the challenges and opportunities for automated monitoring in the tropics we deployed three newly developed cameras systems for monitoring insects, with a focus on night-flying insects. In addition we deployed audible and ultrasound recording equipment. The locations of each device was changed over the 5 day pilot study, and the configuration of each system was changed to allow an assessment of the relative importance of position, light, etc. The study was undertaken on Barro Colorado Island, Panama, in January 2023.</p> <p>Data are aggregated into <em>.zip</em> files, and details of their contents, and metadata are given in <em>read_me.txt</em> and <em>metadata.csv</em>.</p>
Data from: Non-invasive Assessment of Cartilage Damage of the Human Knee using Acoustic Emission Monitoring: a Pilot Cadaver Study
<p>This dataset accompanies the following article: "Non-invasive Assessment of Cartilage Damage of the Human Knee using Acoustic Emission Monitoring: a Pilot Cadaver Study," in <em>IEEE Transactions on Biomedical Engineering</em>, doi: 10.1109/TBME.2023.3263388.</p> <p>Knee acoustic emissions (AE) recorded in the 100-450 kHz and 15-200kHz frequency ranges from a cadaver specimen knee in flexion/extension. Four stages of artificially inflicted cartilage damage and two sensor positions were investigated. </p> <p><em><strong>Stages of artificially inflicted cartilage damage:</strong></em> the cartilage surface damage on the medial compartment, KL III; the cartilage surface damage on the medial compartment plus patellofemoral surface, KL III; the cartilage surface damage on the medial compartment plus on the patellofemoral surface KL IV; the cartilage surface damage on the medial compartment plus on the patellofemoral surface and lateral compartment.</p> <p><strong><em>Sensor positions</em></strong>: medial and lateral knee</p>
Pilot 2 - Research Data
<p>In Pilot 2, data have been collected mainly for the following purposes: [1] achieving the pilots' research objectives, as outlined in the previous section, and [2] managing and monitoring the pilots' implementation and [3] informing technical developments in the ARETE CLB app.</p> <p>In Pilot 2, anonymized student data have been collected in correspondence with the research objective of testing the efficiency of Augmented Reality for STEM education, to evaluate if the CLB apps can help students improve test scores by up to 33 % and increase the retention rate by up to 100 %. Additionally, pedagogical aspects of the AR use and its impact on teaching and learning will be evaluated. To respond to these claims, the methodology applied included pre-tests and post-tests for students and additional qualitative surveys for teachers.</p> <p>Pilot 2 collected only personal information (name, surname, contact information) from the participating teachers, in the form of consent forms (necessary to obtain the participants’ consent to participate in the ARETE research activities). These consent forms have been collected by European Schoolnet (EUN) and stored on EUN’s secure servers. All personal data collected within the project activities will be anonymised upon completion of the project, apart from data that need to be kept for the full audit period.</p> <p>As in the case of Pilot 1, and in accordance with the data minimization principle, no personal information from the participating students or their parents/legal guardians will be transferred to EUN or to other members of the ARETE consortium for the purpose of Pilot 2. The only personal information collected from students and their parents/legal guardians will be for the purpose of acquiring consent/assent, and these forms have been collected at the level of each participating school. EUN has established a Memorandum of Understanding with each participating school in Pilots 2 in order to ensure that:</p> <ul> <li>the heads of the participating schools are appropriately informed about the research carried out in the context of the ARETE project and have provided their agreement for the school to participate in research and</li> </ul> <p>that the schools have all the necessary permissions from the parents/legal guardians of the pupils participating in the ARETE research or, in the case such permissions do not exist at the school level, ensure that the schools collect all necessary consent forms from the participating students. This measure is meant to reduce the transfer of sensitive personal information between organisations unless absolutely necessary.</p>
Pilot 1 - Research Data
<p>Three main aims inform the collection and generation of data in Pilot 1: [1] achieving the pilots' research objectives, as outlined in the previous section, and [2] managing and monitoring the pilots' implementation and [3] informing technical developments in the ARETE WWL app. Anonymised student information has been collected in the form of pre and post intervention standardised tests of reading and spelling competence, in order to compare and evaluate the students’ progress with or without the use of the interactive AR content toolkit. Only the students code, month and year of birth and assessment outcomes will be shared outside the school environment for analysis. The anonymised paper NARA II and VERNON test forms will be stored by participating schools. In addition parents of the students in the intervention and control groups have been requested to complete an anonymised online ‘ProfilED’ Case History form, which is sent directly to WWL for data analysis. The sender's email was not logged nor is there any identifying information on the form. The information gathered in this form is essential for the analysis of Pilot 1 test outcomes as it can identify if certain developmental patterns or comorbid issues enhance or detract from AR intervention outcomes. This data has been securely stored on a server, PC and on an external hard drive (stored in a safe off site).</p> <p>Pilot 1 collects only personal information (name, surname, contact information) from the participating teachers, in the form of consent forms (necessary to obtain the participants’ consent to participate in the ARETE research activities). These consent forms have been collected by European Schoolnet (EUN) and stored on EUN’s secure servers. All personal data collected within the project activities will be anonymised upon completion of the project, apart from data that need to be kept for the full audit period.</p> <p>In accordance with the data minimization principle, no personal information from the participating students or their parents/legal guardians will be transferred to EUN or to other members of the ARETE consortium for the purpose of Pilot 1. The only personal information collected from students and their parents/legal guardians has been for the purpose of acquiring consent/assent, and these forms have been collected at the level of each participating school. EUN has established a Memorandum of Understanding with each participating school in Pilots 1, in order to ensure that: [1] the heads of the participating schools are appropriately informed about the research carried out in the context of the ARETE project and have provided their agreement for the school to participate in research and [2] that the schools have all the necessary permissions from the parents/legal guardians of the pupils participating in the ARETE research or, in the case such permissions do not exist at the school level, ensure that the schools collect all necessary consent forms from the participating students. This measure is meant to reduce the transfer of sensitive personal information between organisations unless absolutely necessary.</p>
Pilot 3 - Research Data
<p>Data collected for the Pilot 3 have been used for the following main purposes:</p> <ul> <li>to develop the contents of the PBIS-AR application (task 5.1 “Analysis of PBIS Requirements for ARETE” of WP5 “Interactive AR for PBIS”)</li> <li>to develop the contents of the PBIS behavioral research lessons (task 5.2 “Analysis of PBIS Requirements for ARETE” of WP5”Interactive AR for PBIS”)</li> <li>to investigate the use of AR as a tool for PBIS, evaluating the incremental value of integrating AR contents within PBIS in promoting school-wide and class-wide positive behaviours (task 6.3 of WP6”Pilots’ Implementation Deployment and Evaluation”).</li> </ul> <p> </p> <p>All collected data can and will be used to realize publications in academic and popular psychological journals, as well as for poster and oral presentation at conferences. The data will consist of:</p> <p>(1) quantitative data: questionnaire and survey data that will be collected through an online and paper-based survey procedure at one or more time points during the pilot (for specifics, see Annex G “06.2022 - ARETE P3 - Assessment Strategy” of D5.3 “WP5 - D5.3: Analysis of PBIS Requirement for ARETE - Update D5.1”) .</p>
Pilot 4 - Research Data
<p>Pilot 4: Teaching with eXtended Reality – Evaluating the MirageXR Authoring Tool</p> <p>Pilot 4 aims to accelerate the uptake of AR in education, by providing an authoring tool and learning management system to teachers. We believe that introduction at scale requires to put tailored authoring tools in the hands of teachers to support the production of bespoke AR learning experiences for the mainstream as well as for the Long Tail. The MirageXR platform is user-friendly and does not require any prior AR experience. Participation is on a voluntary basis, as a choice under free will.</p> <p><strong>Target Group</strong> are teachers who:</p> <ul> <li>have a good level of English language (B2 in Reading and B1 in Writing + Speaking)</li> <li>are pre-service or in-service teachers</li> <li>are willing to participate</li> <li>have an AR-compatible iOS or Android phone or tablet</li> </ul> <p>With this pilot study (ARETE Pilot 4), we seek to evaluate quantitatively and qualitatively, in which ways and how well our authoring toolkit supports teachers in designing XR learning experiences. The ARETE Project Pilot 4 is evaluating novel AR interactive technologies for teaching. The data we gather from this project would help design and develop more effective and efficient digital education solutions and contribute to improving XR Open Educational Resources.</p> <p>In pilot 4, data are collected (1) via online surveys, (2) in interviews, (3) from work products, and (4) from system interaction log files. These data are collected to validate the MirageXR toolkit applied in the pilot and to evaluate the teachers’ experiences during the pilot with regards to their user experience (technical perspective) and to their opinions on the potential role of interactive AR toolkits in teaching and learning processes (pedagogical perspective). The data collected in pilot 4 for these purposes will further be used for scientific publications, e.g., in scientific journals and at conference</p> <p> </p>
Measured data and calculations of pilot RES system for a 103-m2 building in Athens, Greece
<p>This dataset contains complete measurements of an energy system for a building in Athens, Greece (temperatures, flow rates, power, solar radiation, etc.). This system includes a vapour compression heat pump, 4 PVT collectors, a virtual BTES (emulated via a tank with controllable temperature) and three water tanks. A winter and summer day are included. The system operated for space cooling and hot water during the summer day and for space heating and hot water during the winter day.</p> <p>An in-house Python code of NCSR Demokritos has been applied to simulate the energy system operation during these two days for validation purposes. The calculated results are also given in this dataset.</p>
WEFE data collection for a pilot smallholder farm in Costa Rica
<p>In-situ and remote data collection of environmental and socioeconomic data for the planning of integrated water, energy, food, and environment systems for the case study of a smallholder farm in Costa Rica.</p>
Arctic shoreline displacement and validation data for two pilot study areas
<p>Arctic shoreline displacement data for two pilot study areas are supporting information for the paper <em>Nylén, Calle-Navarro and Gonzales-Inca: Arctic shoreline displacement with open satellite imagery and data fusion – Pilot study 1984–2022</em><em>. </em>The two study areas are:</p> <ul> <li>Tanafjorden: a low-arctic meso-tidal fjord coast in mainland Norway</li> <li>Ny-Ålesund: a high-arctic micro-tidal glaciated coast in north-western Svalbard</li> </ul> <p>The study areas are 2500 km² each.</p> <p>The dataset includes following files:</p> <ul> <li><em>Calculating_coastal_landcover_timeseries_summaries.R</em>: code for summarizing the coastal land cover time-series in the R software.</li> <li><em>Calculating_shoreline_timeseries.R</em>: code for calculating the shoreline by fitting and smoothing a polyline to the land cover raster in the R software.</li> <li><em>X_timeseries.tif</em>: a multiband GeoTIFF raster file, with each band describing coastal land cover during one of the eight time-steps (1984–1988, 1989–1993, 1994–1998, 1999–2003, 2004–2008, 2009–2013, 2014–2018 and 2019–2022).</li> <li><em>X_summary.tif</em>: a multiband GeoTIFF raster file, with bands that summarize the time-series from different viewpoints. These summary variables are: probability of belonging to the land class, long-term trend (between 1984-2003 and 2004-2022), change intensity, first time-step in water class, last time-step in water class, first time-step in land class and last time-step in land class.</li> <li><em>X_shoreline.geojson</em>: a GeoJSON vector file, consisting of polylines for the shoreline during each time-step. The attributes of the polyline layer describe the time-step and the total length of the shoreline.</li> <li><em>NyAlesund_timeseries_REDUCED.tif</em>: a reduced time-series for the Ny-Ålesund study area, including only the time-steps with adequate number of observations (i.e., excluding 1984–1988, 1994–1998 and 2004–2008).</li> <li><em>X_reference_shoreline.geojson</em>: a GeoJSON vector file including the manually digitized (scale 1/5000) reference shoreline corresponding to the time-step 2019–2022.</li> <li><em>X_validationpoints.geojson</em>: a GeoJSON vector file including 2000 random points (within 2 km from the reference shoreline) that have been manually classified into water and land. The classification corresponds to the time-step 2019–2022.</li> </ul>
The effect of dietary bioactive on gut microbiome diversity (DIME) – a pilot study
<p>The DIME study consists of a randomised 2x2 cross-over human intervention where healthy participants (n = 20) are subjected to a diet high in bioactive-rich food for two weeks and a diet low in bioactive-rich food. There is a four-week washout between the two interventions. </p> <p>The continuous glucose monitoring was achieved using the Abbott freesylte libre flash glucose device. The baseline of the participants were determined 7 days before the start of the intervention, followed by the first arm and second arm. The period between the two arms (washout) was not recorded.</p> <p>We also included sleep data which consists of the amount of time spent in bed and during that time the amount of time spent in light, deep and rem in all 20 participants during the course of the dietary intervention, both the high and low bioactive diet. that was captured using Fitbit wearables during both stages of the dietary intervention,</p>
Interviews Questionnaire Pilot Islands
<p>Anonymized data set from questionnaires and interviews carried out in the three pilot islands in REACT project.</p>
Results of the test for the sampling period described in D7.3: Madrid pilot report
<p>This datasets provides further information about section 9.4 Monitorisation and results of D7.3. This public deliverable describes Madrid pilot project report. CARTIF share this dataset with the results of the test carried out during the sampling period regarding particulate matter and nitrogen oxides.</p>
Database 2 - Second-year samplings in the CodeRe-farm pilot sites in Greece
<p>This database contains the results of the second year of AWIN assessments and general data recordings in the Greek goat pilots participating in the Code: Re-farm research project.</p>
Annual measurements of vegetation canopy and basal gap intercepts from the three Conmod Pilot study locations at Jornada Basin LTER, 2008-2016
This data package contains annual measurements of vegetation canopy and basal gap sizes from transects at the Connectivity Modifier (Conmod) Pilot study on the Jornada Experimental Range from 2008-2016. There were 3 sites for this study: Gravelly Ridges, Aeolian, and Dona Ana. Within each site, there were 8 study plots, 4 of which were treatment plots where connectivity modules (conmods) were installed to decrease gap sizes between perennial vegetation. The plots were 8 x 8 meters and had an 8 x 8 meter buffer zone on both sides of the plot (upwind and downwind). Beginning in 2008, vegetation canopy and basal gap sizes were collected annually in all plots using the gap intercept method. These data were collected in 2008-2010, 2012 and 2016. At each plot, four parallel 24-meter transects crossing the upwind buffer, the plot, and the downwind buffer were measured. These parallel transects were spaced at 0.8, 2.8, 4.3, and 7.2 meter intervals across the plot and buffer areas. This study is complete (finished in 2016) and was the pilot study to the newer Cross Scale Interactions Study.
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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.