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8,020 results for “pilot”
MED-GOLD Indicators for the Wine pilot service over Iberian Peninsula from ERA5 Reanalysis 1979-2020
<p>Indicators of interest for the Wine sector over the Iberian Peninsula using ERA5 Reanalysis data for the period 1979 to 2020:</p> <ol> <li>Growing Season Temperature (GST) [Temp averaged between April and October]</li> <li>Spring Rain (SprR) [ Precip cumulated between 21 apr and 21 Jun],</li> <li>Harvest Rain (HarvestR) [ Precip cumulated between 21 aug and 21 Oct]</li> <li> (SU35) -number of days with temperature higher than 35°C [for April ot October],</li> <li>Warm Spell Duration Index (WSDI) [days with at least 6 consecutive days when the daily temperature maximum exceeds its 90th percentile for April to Oct]</li> </ol> <p>Wine risk indicators, implemented specifically for the MED-GOLD Wine pilot service by ENEA and SOGRAPE VINHOS S.A.:</p> <ol> <li>Sanitary Risk Index= offgts*offsp*(percentile(SprR))+offhart*percentile(HarvestR)+(100-percentile(GST); with offhart=1.; offsp=1; offgts=1.; if percentile (SprR)>= 60; offsp=1.5; if percentile (GST)<= 40; offhart=1.5; if percentile (GST)>=70; offgts=1.5; where the percentile are here computed starting from the distribution over the 1993-2106 hindcast period to be consistent and comparable with the seasonal forecast</li> <li>Heat Risk Index = percentile(GST)+percentile(SU35_AMJJASO) + percentile(WSDI_AMJJASO),where the percentile are here computed srarting from the distribution over the 1993-2106 hindcast period to be consistent and comparable with the seasonal forecast</li> </ol> <p>Datasets computed by ENEA, in collaboration with SOGRAPE VINHOS S.A. in the framework of the European MED-GOLD project, funded from the European Union's Horizon 2020 Research and Innovation programme under Grant agreement No. 776467</p>
MED-GOLD Indicators for the Wine pilot over Douro Valley based on High Resolution Climate projections
<p>Indicators of interest for the Wine sector over the Douro Valley using high resolution climate projections.</p> <ol> <li>GDD (Growing Degree Days) - summation of daily differences between daily temperature averages and 10 for the period April-October</li> <li>GST (Growing Season Temperature) - average of daily average temperatures for the period April-October</li> <li>SprR (Spring Rain) - Precipitation accumulated between 21st April-to 21st June,</li> <li>HarvR (Harvest Rain)-Precipitation accumulated between 21 August and 21 October</li> <li>SU35 -number of days with temperature higher than 35°C for the period April-October,</li> <li>WSDI (Warm Spell Duration Index) -days with at least 6 consecutive days when the daily temperature maximum exceeds its 90th percentile for the period April-October.</li> </ol> <p>The results are based on an sub-ensemble of five RCMs from the EURO-CORDEX modelling experiment which have been statistically downscaled to 1km x1km horizontal resolution using the PTHRES gridded dataset as the reference dataset. More details can be found in Raül Marcos-Matamoros, (2018). Report on the methodology followed to implement the wine pilot services. Zenodo. https://doi.org/10.5281/zenodo.4543337</p> <p>Datasets computed by National Observatory of Athens, in collaboration with SOGRAPE VINHOS S.A. in the framework of the European MED-GOLD project, funded from the European Union's Horizon 2020 Research and Innovation programme under Grant agreement No.776467</p> <p> </p> <p> </p>
Data - Co-feeding of VGO and pine-wood derived hydrogenated pyrolysis oils in an Fluid Catalytic Cracking pilot plant to generate olefins and gasoline
<p>Dataset to the corresponding research article with the same title. Research article is submitted to the open research europe platform.</p>
PAsCAL WP6 Pilot 5 Vulnerable Travellers in Connected Transport Environments Video
<p>A summary video documenting and introducing the activities of pilot 5 of the PAsCAL real-world pilots. The part of the pilot which is documented in the video took place within the wider Madrid transport network between May and November 2021. The participants tested the Apertum mobile application (https://apertum.world/) and three groups of potential users were recruited:</p> <ol> <li>Elderly citizens (65+ years), via the non-for-profit organisation Nadiesolo;</li> <li>Paraplegic citizens with permanent mobility constraints and in many cases reduced hand mobility, via the medical centre FLM (Fundación Lesionado Medular);</li> <li>Citizens who were confronted with temporary mobility constraints (e.g., carrying heavy luggage, baby strollers, using crutches or wheelchairs to simulate temporary injuries).</li> </ol>
PAsCAL WP6 Pilot 5 Experience of Vulnerable Travellers in Connected Transport Environments
<p>These three datasets were collected within the context of the PAsCAL research project between May and November 2021. Each survey included a different user group and all activities were conducted in different places:</p> <ol> <li>Apertum Testing: 165 individual testers used the Apertum (https://apertum.world/) mobile application for step-free public transport information. These participants consisted of elderly persons (65+ years old), paraplegic users and persons with temporary mobility constraints (like injuries or persons who use baby strollers or heavy luggage). The testing took place in 3 different locations and followed 3 different scenarios to ensure that the routing or departure point of the user does not influence their acceptance of the CAV solution;</li> <li>UI-UX Testing: The same application was tested by 20 additional persons (half of them digital natives and half of them digital nomads) in Madrid, Spain. For the UI-UX testing, a few standard tasks were defined and the participants were able to familiarise themselves with the application for 30 minutes before the testing started. The WAMMI methodology was applied, consisting of a survey that is to the point.</li> <li>Focus Discussion Groups: The FDGs took place across 4 cities in Italy (Rome, Bologna, Milan and Naples) and included a diverse range of 51 blind and partially sighted persons. The participants attended a short briefing about autonomous and connected mobility and shared their experiences, opinions and attitudes freely before completing a survey to report on their freedom of travel, their needs and attitudes towards CAVs.</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>
PAsCAL WP6 Pilot 4 Shared Connected Transport Video
<p>A summary video documenting and introducing the activities of pilot 4 of the PAsCAL real-world pilots. </p> <p>The "Shared Connected Transport" pilot explores the role of CAVs within commercial shared mobility businesses in close cooperation with the luxembourgish car-sharing operator Moovee and the transport operator Sales-Lentz to explore the viability and profitability of the integration of CAVs into their offering. In particular, this pilot seeks to explore the possibility of a wide market uptake of automated vehicles within existing fleets in urban environments.</p>
PAsCAL Pilot 2 Autonomous Driving Training
<p>The video documents and describes the activities of Pilot 2 "Autonomous Driving Training" of the PAsCAL vreal-world pilots. This pilot took place at the ACI-Sara Safe Driving Centre in Lainate, near Milan (Italy) on February 2022. It involved trainers, professional and experienced drivers to test the study on a new driving training methodology to encourage people to accept the transition from conventional cars with partial autonomous driving capabilities to fully autonomous cars. </p> <p> </p>
Perception and Experience of Appraisals and Consumption Emotions in Reviews (PEACE-Reviews) Pilot Dataset
<p>This pilot dataset contains review text responses and associated emotional ratings about the usage of particular products by participants (USA) recruited from a crowdsourcing platform called Prolific. This pilot study is part of a larger project of constructing a dataset that has people's review text of particular products labelled with emotional variables such as cognitive appraisals, and emotional intensity. The aim of this pilot is to pilot different methods that elicit participants to write their emotional experiences when using a product. </p> <p><strong>Variables</strong></p> <p>Column A- condition that the participant was assigned to. [1- presence of emotion prompts, review format; 2- presence of emotion prompts, question format; 3- absence of emotion prompts, review format; absence of emotion prompts, question format]</p> <p>Column B- product reviewed</p> <p>Column C- cost of product reviewed</p> <p>Column D- emotion felt while using the product (this only applies to condition 1 and 3)</p> <p>Columns E - K are the text responses from participants.</p> <p>Column E- review text responses of the product (this only applies to condition 1 and 3)</p> <p>Column F- how important is the product in text (this only applies to condition 2 and 4)</p> <p>Column G- how did the participants feel when using the product in text (this only applies to condition 2 and 4)</p> <p>Column H- Why did the participants feel the way they are feeling when using the product in text (this only applies to condition 2)</p> <p>Column I- whether the product is consistent with what the participants' wanted in text (this only applies to condition 2 and 4)</p> <p>Column J- whether using the product was pleasant in text (this only applies to condition 2 and 4)</p> <p>Column K- whether the participants' understood what was happening when they were using the product in text (this only applies to condition 2 and 4)</p> <p>Columns L - AE are the rating of the cognitive appraisals on a Likert scale of 1-7. (7 means that the participants endorse more on that appraisal). These ratings correspond to how the participants' perceive the situation of using the product that they have talked about. Missing data correspond to the participants' rating as 'not applicable'. </p> <p>Column L- self-control- To what extent did you think you had control over the situation?</p> <p>Column M- pleasantness- To what extent did you think that the situation was pleasant?</p> <p>Column N- goal congruence- To what extent was the situation consistent with what you wanted?</p> <p>Column O- expectedness- To what extent did you expect the situation to occur?</p> <p>Column P- fairness- To what extent did you think the situation was fair?</p> <p>Column Q- certainty- To what extent did you understand what was happening in the situation?</p> <p>Column R- coping potential- To what extent were you able to cope with any negative consequences of the situation?</p> <p>Column S- goal relevance- To what extent did you think that the situation was relevant to what you wanted?</p> <p>Column T- other-agency- To what extent did you think that someone else other than you was responsible for what was happening in the situation?</p> <p>Column U- difficulty- To what extent did you think that the situation was difficult?</p> <p>Column V- self-agency- To what extent did you think that you were responsible for what was happening in the situation?</p> <p>Column W- attentional activity- To what extent did you think that you needed to attend to the situation further?</p> <p>Column X- circumstances-control- To what extent did you think that circumstances beyond anyone's control were controlling what was happening in the situation?</p> <p>Column Y- positive future expectancy- To what extent did you think that the situation would get better?</p> <p>Column Z- other-control- To what extent did you think that other people were controlling what was happening in the situation?</p> <p>Column AA- effort- To what extent did you think that you needed to exert effort to deal with the situation?</p> <p>Column AB- problems- To what extent did you think that there were problems that had to be solved before you could get what you wanted?</p> <p>Column AC- external normative significance- To what extent did you think that the situation was consistent with external and social norms?</p> <p>Column AD- circumstances-agency- To what extent did you think that circumstances beyond anyone's control were responsible for what was happening in the situation?</p> <p>Column AE- familiarity- To what extent did you think that the situation was familiar?</p> <p>Columns AF - AH are some variables pertaining the participants' experience of using the product on a Likert scale of 1-7.</p> <p>Column AF-How much effort did you put into researching about the product/service before purchasing it?</p> <p>Column AG- To what extent would you recommend the product/service that you have recalled to someone else?</p> <p>Column AH- To what extent would you purchase again the product/service that you have recalled?</p> <p>Columns AI - AP are the emotional intensity rated for each emotion on a Likert scale of 1-7 (7 means that the participant strongly felt that emotion when using the product)</p> <p>Columns AQ - AW are demographic variables (note that participants are recruited from USA)</p> <p>Column AQ- gender</p> <p>Column AR- age group</p> <p>Column AS- race</p> <p>Column AT- whether the participant is of Spanish, Hispanic, or Latino origin</p> <p>Column AU- native language</p> <p>Column AV- highest education level</p> <p>Column AW- total household income before tax during the past 12 months</p>
Database 1 - First-year samplings in the CodeRe-farm pilot sites in Greece
<p>This database contains the results of the first year of AWIN assessments and general data recordings in the Greek goat pilots participating in the Code: Re-farm research project.</p>
SocketSense Össur DS01 Pilot Study Shear & Pressure
<p>SocketSense Pilot Study Shear & Pressure Dataset</p> <p>Intra-socket pressure data collected from QTSS sensors at the socket-liner interface for various tasks.</p> <p>MAT file contains structure of pressure, shear, and gait monitor data collected from the SocketSense system. Shear values reported in microSiemens. Pressure values reported in kPa.</p> <p>Structure format:</p> <p>For shear</p> <ul> <li>data--task--sensor strip location--shear direction</li> </ul> <p>For pressure</p> <ul> <li>data--task--sensor strip location--sensel location pressure</li> </ul> <p>See figure for visual representation of strip and sensel locations in the socket.</p> <p>Tasks include donning and level ground walking</p> <p>Gait state is represented numerically as follows:</p> <p>[1] Unknown</p> <p>[2] Toe-Off</p> <p>[3] Mid-Swing</p> <p>[4] Heel Strike</p> <p>[5] Mid-Stance</p> <p>Time vector is given in seconds. Sampling frequency was 20Hz.</p>
SocketSense Össur DS01 Pilot Study
<p>SocketSense Pilot Study Pressure Only Dataset</p> <p>Intra-socket pressure data collected from QTSS sensors at the socket-liner interface for various tasks.</p> <p>MAT file contains structure of pressure and gait monitor data collected from the SocketSense system. Pressure values reported in kPa.</p> <p>Structure format:</p> <ul> <li>data--task--sensor strip location--sensel location pressure</li> </ul> <p>See figure for visual representation of strip and sensel locations in the socket.</p> <p>Tasks include donning, doffing, walking, ramp, sit-to-stand/stand-to-sit, etc.</p> <ul> <li>sts = sit to stand</li> <li>sts includes sit-to-stand and stand-to-sit</li> <li>ramp includes ramp ascent and descent</li> </ul> <p>Gait state is represented numerically as follows:</p> <p>[1] Unknown</p> <p>[2] Toe-Off</p> <p>[3] Mid-Swing</p> <p>[4] Heel Strike</p> <p>[5] Mid-Stance</p> <p>Time vector is given in seconds. Sampling frequency was 20Hz.</p>
NAIADES Project - Carouge pilot
<p>The city of Carouge, Switzerland, has been involved from 2019 to 2022 in the NAIADES project, as one of the three pilot cities. The NAIADES H2020 European project aims at improving water management in cities. Within the Carouge pilot, the project focused on two use cases: watering of the city's flowerbeds and management of the water quality in fountains and paddling pools.</p> <p>Credits for drone images: Staprod</p> <p>Music : “Electronic base and pop guitar” by Frankumjay (Creative Commons 4.0 - <a href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbGUwbjNEdXRMcHNqVC1iaUcxdU1aZ3hsVldZd3xBQ3Jtc0tuOTFDYkRUYmtJSzJWRzJIVE1FaGVxbU9oRDUtSTBUZmUwLXlRNlhtaXpzbTNmZW54VU91RzI0SDlhY3dzcFN0WXVyZjRCTEo0elpacDhtbXZIMDVDcGctZW1WNzBiWEx4bEgxWC1qb0U3cmpwWGs3Yw&q=https%3A%2F%2Ffreesound.org%2Fpeople%2Ffrankum%2Fsounds%2F273300%2F&v=yYPuRduAR8Q">https://freesound.org/people/frankum/...</a>)</p> <p>Picture : “A Naiad or Hylas with a Nymph” by John William Waterhouse (1893)</p>
TRANSFORM video - First-person experiences of endometriosis in Catalonia (health pilot)
<p>Endometriosis affects 10% of girls, women (and sex-gender diversities) of reproductive age worldwide. Its diagnosis takes between 8 and 10 years from the onset of symptoms, a time during which these people suffer serious physical, psychological, social and professional consequences. Participatory research on endometriosis has resulted in the co-creation of 6 general and 28 specific recommendations, written in the first person by patients, with the aim of informing public health policies and contributing to improving the clinical approach and care services to patients.</p> <p>Results: https://zenodo.org/record/7248108#.Y-IiEC0w2X1</p> <p>TRANSFORM project has received funding from the European Union's Horizon2020 research and innovation programme under grant agreement No 872687. This video reflects only the author's view and the funding organisations are not responsible for any use that may be made of the information it contains.</p>
TRANSFORM video - Waste pilot using citizen science in Catalonia
<p>A citizen science project to improve the selective waste collection system in Mollet del Vallès Citizens have a key role in political decision-making and the TRANSFORM pilot project is a clear example of this. TRANSFORM Catalan cluster carried out a citizen science pilot in the city of Mollet del Vallès to contribute to improving the selective collection of municipal waste. Citizen science was used as an instrument to move towards a greener, more digital, resilient and fairer socioeconomic model.</p> <p>Results of the pilot project: https://zenodo.org/record/7472655#.Y-IMXS0w2X2</p> <p>TRANSFORM project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 872687. This video reflects only the authors' view and the funding institutions are not responsible for any use that may be made of the information it contains.</p>
China's new type rural social insurance pension (NRSIP): transposition central government pilot guidelines into local documents
<p>The list provides an overview of how the pilot guidelines issued by the central government have been transposed into local documents (provincial level).</p> <p>Matthias Stepan has compiled the data and information in the period 2009-2013. It has been part of his dissertation project at VU University Amsterdam.</p>
Advanced photochemical processes for the manufacture of nanopowders: an evaluation of long-term pilot plant operation
<p>Dataset for the publication:</p> <p>Advanced photochemical processes for the manufacture of nanopowders: an evaluation of long-term pilot plant operation</p> <p>React. Chem. Eng., 2022, 7, 968–977</p> <p>DOI: 10.1039/d1re00374g</p>
The Transformative Storytelling Technique for Developing Guided Digital Narratives in Mental Health Support. Pilot study
<p>In a society where advances and innovations occur on a daily basis, the development of digital mental health tools continues at almost uncontrollable rates. Following trends in the utilization of storytelling across fields and reflecting on the lack of working models and frameworks for the application of storytelling for mental health support, the Transformative Storytelling Technique (TST) is designed and developed as a part of this work. This technique represents a new category for creating hybrid content to guide the experience of audiences, starting with the case of informal caregivers. In this pilot study, a TST caregiver story is assessed pre-post in an interventional study, investigating the potential role of mental health audio stories in supporting informal caregiver wellbeing. The anonymized raw dataset is available.</p>
Effect of unattended distributional training on phoneme category discrimination in English-Mandarin bilingual adult participants in Singapore (Pilot Study Data)
<p>Datasets from Pilot study of <strong>Effect of unattended distributional training on phoneme category discrimination in English-Mandarin bilingual adult participants in Singapore</strong> (project linked here: https://doi.org/10.17605/OSF.IO/S6VDN).</p>
Carouge pilot: open data for the weather
<p>Datasets collected in the NAIADES Carouge pilot and containing all the data related to the weather.</p>
Dataset for "Exposure and environmental engagement: A pilot integrating wearable sensors, air quality and citizen science"
<p>The dataset contains anonymised readings of 7 citizens taking air quality measurements using PlumeLabs Flow 2 monitor. Data is for Falmouth/Penryn, and Bristol and it was collected between January 26, 2022 and March 9, 2022.</p> <p>CSV file:</p> <ul> <li>latitude: unit degrees, positive values indicate North hemisphere.</li> <li>longitude, unit degrees, positive values indicate East.</li> <li>AQI: PlumeLabs' Air Quality Index.</li> <li>site: A refers to Falmouth/Penryn(UK), B refers to Bristol (UK).</li> <li>count: auxiliary variable that indicates that the record was comprised of a single reading.</li> </ul> <p>Jupyter notebook: The air quality analysis was conducted with Python 3.9.16 alongside numpy 1.24.3, pandas 2.0.2, matplotlib 3.7.1, and cartopy 0.21.1 (background tiles by OpenStreetMaps).</p>
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.