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80 results for “bicycle”
Bicycle Mobility Data: Current Use and Future Potential. An International Survey of Domain Professionals
<p>Active mobility, especially cycling, is an essential building block for sustainable urban mobility. Public and private stakeholders are striving to improve conditions for cycling and subsequently increase its modal share. Data are regarded as key for different measures to become efficient and targeted. There is extensive evidence for an increasing amount of mobility data, availability of new data sources and potential usage scenarios for such data. However, little is known about the current use of these data in policy making, planning and related fields. To the best of our knowledge, it has not been investigated yet to which degree professionals in the broader field of cycling promotion benefit from an increasing amount of cycling-related data. Thus, we conducted a multi-lingual online survey among domain professionals and acquired data on their perspectives on current data availability, use and suitability as well as the potential they see for the use of cycling data in the future. In total, we received 325 complete responses from 32 countries, with the vast majority of 241 valid responses originating from Germany, Austria and Italy. Key findings are: 84% of domain professionals attribute high importance to data, and 89% state that they currently cannot or only partly solve their tasks with the data available to them. Results emphasize the need for making more and better suited data available to professionals in cycling-related positions, in both the private and public sector.</p> <p>Read the full publication: <a href="https://doi.org/10.3390/data6110121">https://doi.org/10.3390/data6110121 </a></p>
Bicycle trips collected using Cyclists Geo-C geo-game
<p>This is an experimental dataset for the bicycle trips recorded using and geo-game called "Cyclist Geo-C". It contains the geometry of the trips recorded by 60 participants from three European Cities: Münster, Germany; Castelló, Spain; Valletta, Malta. This dataset was collected and analysed for the PhD Thesis "Mobile Services for Green Living" part of the European Joint Doctorate in Geoinformatics and the <a href="http://geo-c.eu/">Geo-C </a>Project. </p> <p>The dataset is composed of three subsets.</p> <ol> <li>There is a point dataset called "<em><strong>trips_od.geojson</strong></em>" which contained the point geometries where each trip started and ended with attributes for latitude, longitude, altitude, and precision coordinates. Each point also had the timestamp which indicates the time when the user started or ended the trip.</li> <li>There is a line dataset called "<em><strong>segments.geojson</strong></em>" which contained the geometries of the straight lines connecting two locations of the participant. Each segment started from an initial point "p<sub>i</sub>" recorded at a "t<sub>i</sub>” and ended at the next point recorded by the user "p<sub>f</sub>” at time “t<sub>f</sub>”. The time difference between "t<sub>i</sub>” and “t<sub>f</sub>” was at most five minutes while the length of the segment was at most one kilometre. Each segment also had the participant and trip identifier, and the segment's sequence number within the trip For each of the trip segments, we calculated the distance and speed using the recorded coordinates and timestamps from "p<sub>i</sub>" and "p<sub>f</sub>" points. <span class="math-tex">\(trip\_segment = f(p_i,p_f)\)</span> and <span class="math-tex">\(segment\_speed = \frac{distance(p_i,p_f)}{\Delta time(p_i,p_f)}\)</span>. Then we classified the segments according to the calculated distance as: “<em>walking segment</em>” when the calculated speed was less than 5 km/h; “<em>cycling segment</em>” when the calculated speed was between 5 and 50 km/h; or “<em>non-cycling segment</em>” when the calculated speed was more than 50 Km/h.</li> <li>There was another line dataset called “<em><strong>trips_tags.geojson </strong></em>” which contained the geometries of each of the trip paths. A trip was a line (also called polyline by GIS users) defined by the ordered sequence of trip segments. It started from origin point "p<sub>i</sub>" of the trip’s first segment and ended at the destination point "p<sub>f</sub>" of the trip's last segment. Each trip also had the participant's identification, trip's identification, the number of segments, start and end times.</li> </ol> <p>In addition to the experimental dataset recorded by participants, our analysis used a secondary dataset to define a comparable framework for the three cities. The secondary dataset consisted of the existing bicycle paths in the cities of Münster and Castelló as well as the planned bicycle paths around Valletta. For the city of Münster, the source of the bicycle paths was the <a href="http://www.openstreetmap.org">OpenStreetMap</a> (we downloaded the line elements with the tags “<em>bicycle=yes</em>” and "<em>cycleway=yes</em>”). For the city of Castelló, we obtained the bicycle paths from the city transport authority, including the city of Valletta, we created a digital version of the national bicycle network plan.</p> <p>We estimated the number of trips "<em><strong>bikepaths_trips.geojson</strong></em>" and the number of segments "<em><strong>bikepaths_segments</strong></em><em><strong>.</strong></em><em><strong>geojson</strong></em>" at each bike path. Also, we provide the areas where participants faced frictions during the experiment which corresponded to low cycling speeds "frictions.geojson".</p> <p>Finally, we provide a visual reference of the dataset in "<em><strong>frictions_cities.pdf</strong></em>".</p>
Fremont Bridge Hourly Bicycle Counts by Month October 2012 to present
<p>The Fremont Bridge Bicycle Counter records the number of bikes that cross the bridge using the pedestrian/bicycle pathways. Inductive loops on the east and west pathways count the passing of bicycles regardless of travel direction. The data consists of a date/time field: Date, east pathway count field: Fremont Bridge NB, and west pathway count field: Fremont Bridge SB. The count fields represent the total bicycles detected during the specified one hour period. Direction of travel is not specified, but in general most traffic in the Fremont Bridge NB field is travelling northbound and most traffic in the Fremont Bridge SB field is travelling southbound.</p>
Urban bicycle networks, existing and synthetically grown
<p>This data set contains all data used and generated in the study Growing Urban Bicycle Networks. The data contain, for 62 cities: original data of bicycle and street networks acquired from OpenStreetMap using OSMnx, processed simplified and merged data of these networks and snapped points of interests of rail and metro stations and grids, results and metrics of simulated synthetic bicycle networks, plots of results, plots of existing and synthetic networks, videos of simulated growing synthetic bicycle networks.</p>
Data for "The Heritage Digital Twin: a bicycle made for two."
<p>The file contains the data used in the case studies of the paper "The Heritage Digital Twin: a bicycle made for two. The integration of digital methodologies into cultural heritage research" published on ORE.</p>
Bicycle unuse
<p>The dataset summarises the reasons why Spanish people above 16 do not move on foot or by bike, grouped by comunidades autonomas</p>
Bicycle not use
<p>The curated version of the dataset listing the reasons why people above 16 do not use bicycle to move.</p> <p>The separators are ";" and decimal are indicated by point (e.g. 1.55).</p>
Livorno, Urban driving, Automated vehicle detects fallen bicycle
<p><strong>Scenario description</strong>:</p> <p>Test session for AD+connected car and connected cars approaching a fallen bicycle.</p> <p><strong>Session description</strong>:</p> <p>The fallen bicycle use case aims to demonstrate the possibility for a vehicle to detect in advance, using V2X communication, the presence of a fallen bicycle on the road. In case of fall, the bicycle signals its presence to the other vehicles using DENM messages. The AD car publishes the detected event to the oneM2M and safely reduces its speed until to stop. Goal is to record data for the technical evaluation.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Urban Driving in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_V2X_all</strong>: V2V messages during platooning sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Urban Drining in Livorno.</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.</p>
Livorno, Urban driving, detection of fallen bicycle
<p><strong>Scenario description</strong>: Test session for fallen bicycle with AD and connected vehicle</p> <p><strong>Session description</strong>: The fallen bicycle use case aims to demonstrate the possibility for a vehicle to detect in advance, using V2X communication, the presence of a fallen bicycle on the road. In case of fall, the bicycle signals its presence to the other vehicles using DENM messages. The AD car safely reduces its speed and stops.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_Vehicle_all</strong>: Data generated from the vehicle sensors</p> <p>This dataset refers to the vehicle datasets generated from the vehicle sensors during Urban Driving in Livorno. This includes the data coming from the CAN bus and GPS. It includes following kind of dataset: Vehicle: general data (speed, battery); PositioningSystem: data from GPS; VehicleDynamics: data about dynamic (acceleration...); LateralControl: steering and lane control data</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_V2X_all</strong>: V2V messages during platooning sessions</p> <p>This dataset refers to the V2V messages exchanged between ITS stations (vehicles and RSUs) during the Urban Drining in Livorno.</p> <p><strong>AUTOPILOT_Livorno_UrbanDriving_IoT_all</strong>: Data extracted from IoT oneM2M platform</p> <p>This dataset refers to messages exchanged by Urban Driving devices, applications and services across the oneM2M platform.</p>
Online Repository of the Study "I want to RIDE my e-bicycle!": Supporting Developers Categorizing User Issues of a Mobility-as-a-Service Platform
<p><strong>Online Repository of the Study </strong><em>“I want to RIDE my e-bicycle!": Supporting Developers Categorizing User Issues of a Mobility-as-a-Service Platform</em></p> <p><strong>Introduction</strong></p> <p>In the Mobility-as-a-Service (MaaS) context, e-bikes are important and environmental-friendly transportation resources providing flexibility, time and cost savings, and reducing traffic congestion. Additional to user satisfaction and marketing advantages, the resolution of user-reported issues is regulated in many cities. In order to efficiently solve the issues, it is essential to quickly identify their types (e.g., software- or hardware-related?) to assign them to the responsible team. But for popular e-mobility services, the manual analysis of the reports is inefficient because of its tediousness, high time requirements, and error-proneness. </p> <p>Our empirical study, carried out in the context of a <em>Mobility as a Service </em>start-up company, proposes an approach for the automated identification of relevant concerns reported by users of e-bike services. The company has more than 20,000 private customers across seven different countries and dedicates considerable effort in analyzing user behavior. However, the current manual process of analyzing and triaging user-reported issues hinders MaaS-company’s ability to grow and expand its services. </p> <p>To help MaaS providers identify relevant user-reported issues, In the study, we (i) manually inspect about 3,000 user-reported issues received by the MaaS company; (ii) design a taxonomy modeling the types of relevant issues reported by users; and (iii) propose MaaS-RIDE, an approach to automatically classify the user-reported issues according to the categories of the devised taxonomy. </p> <p>Our results demonstrate that MaaS-RIDE is able to accurately (F-measure ≥ 93%) identify software and hardware user-reported issues. This result is critical for e-bike sharing companies to address such issues in an agile way and achieve the required user satisfaction.</p> <p><strong>Dataset Overview</strong></p> <p>The dataset is composed of the following different sorts of data: </p> <ul> <li> “<em>Data_and_preprocessing</em>” folder <ul> <li>o the user-reported issues data</li> <li>o the user-reported issues data processed as Bag of Words for Machine Learning training. <ul> <li>For this look at the sub-folder “<em>input_data_for_ML</em>” and the following matrices: <ul> <li><em>tf-idf-matrix-of-comment_finals_with_oracle_info_low_level.csv</em></li> <li><em>tf-idf-matrix-of-comment_finals_with_oracle_info.csv</em></li> </ul> </li> <li>Moreover, a sample of selected issues was reported in the replication package: <ul> <li>see file “<em>randomSamples.csv</em>” (due to a non-disclosure agreement with our industrial partner, we are unauthorized to share the whole raw user reports used in our experiments)</li> <li> “RQ1” folder: Types of E-bikes User-reported Issues</li> </ul> </li> </ul> </li> <li> the resulting taxonomy after the analysis of the issues</li> <li> “RQ2” folder: Classifying E-bikes Issue types</li> <li> the trained models </li> <li> the results of the models</li> </ul> </li> </ul> <p>The following sections describe more in detail what each of those folders and files contain.</p> <p><strong>“Data_and_preprocessing” folder</strong></p> <ul> <li><strong>User-reported issues subset.</strong></li> </ul> <p>In an industrial setting, due to privacy reasons, we disclose only an example subset of the user-reported issues, this information is in the file <em>randomSamples.csv</em>.</p> <p>The <em>randomSamples.csv </em>a subset that was generated randomly adding 20 examples using a stratified sampling from the High-level categories and 20 from the Low-level categories. This subset is not exhaustive but serves the purpose of showing the reviewers the kind of issues that this particular industrial set is confronted with. The file contains:</p> <ul> <li> <ul> <li> the Id of the user report; </li> <li> the column "comment_final"<strong> </strong>contains the issue text after the replacement of information that needed anonymization (e.g., vehicle-plates, personal names, addresses and timestamps); </li> <li> the column "High_level_category" contains the selected category from the 5 first level categories of the presented <em>Three-level taxonomy of e-bike user reported issues</em>; </li> <li>• the columns ‘Low_level_category" and "Fine_grained_topic" contain the assigned, if existing, respective category. </li> </ul> </li> <li><strong>Bag of Words Term by Document matrix.</strong></li> </ul> <p>An important input for training the ML models is the Bag of Words representation generated after processing the 2,989 manually-labeled user issues. The result of this process is a Term-by-Document matrix. We share this matrix in the files in the sub-folder <em>input_data_for_ML </em>where they are labeled for High- and Low-level categories. </p> <p>In the <em>tf-idf-matrix-of-comment_finals_with_oracle_info.csv</em> and <em>tf-idf-matrix-of-comment_finals_with_oracle_info_low_level.csv</em> files, the first column refers to the issue “Id”, the last column “oracle” is the labeled category, the rest of the columns represent the terms contained in the 2,989 user-reported issues and in each row the weight of the i−𝑡ℎ term contained in the j−𝑡ℎ user issue by using the tf-idf score.</p> <p><strong>“RQ1” folder</strong></p> <ul> <li><strong>“Three-level taxonomy of e-bike user-reported issues.pdf<em>” file</em></strong></li> </ul> <p>The taxonomy derives from the manual analysis of the 2,989 user issues. We found that a three-level taxonomy provides significant granularity to the MaaS-company. The taxonomy encompasses 5 High-level categories, 16 Low-level categories, and 15 Low-level subcategories of e-bike user-reported issues. The file <em>Three-level taxonomy of e-bike user-reported issues.pdf</em> presents the taxonomy categories and in the columns “Nr.” and “%” it shows the number of occurrences within the analyzed dataset, and the corresponding percentages.</p> <p><strong>“RQ2” folder</strong></p> <ul> <li><strong>“Trained Models” folder</strong></li> </ul> <p>We provide the trained machine and deep learning models in the sub-folder <em>ML_DL_models</em>. Our approach experimented with classic machine learning models based on the Bag-of-Words approach using SVM, on Word Embeddings using FastText, and Language models leveraging BERT. The SVM and BERT models were trained using the open source low-code data analytics platform KNIME and were used to classify issues corresponding to the first and second levels of the taxonomy from the “RQ1” folder. A 10-fold cross validation strategy was used to assess the classification performance. </p> <p>The fastText model was trained by using default values of parameters (https://fasttext.cc/docs/en/options.html) and a 10-fold cross-validation strategy. With fastText, we classified issues corresponding only to the first level of the taxonomy from “RQ1” folder, since fastText is more effective when more data points are available in the training set (i.e., lower levels in the taxonomy have fewer well-represented issue types).</p> <ul> <li><strong>“Model results” folder</strong></li> </ul> <p>In the sub-folder model_results we provide the tables summarizing the results of using the proposed MaaS-RIDE approach, with which we automatically identify and categorize user-reported issues according to the High-level and Low-level categories of the taxonomy devised in RQ1, which are relevant for the MaaS-company. </p>
Dataset: Bicycle Therapeutics plc (BCYC) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Molecular Determinants and Pharmacological Analysis for a Class of Competitive Non-transported Bicyclic Inhibitors of the Betaine/GABA Transporter BGT1: Modeling Data
<p>This archive contains the modeling data for the study <a href="https://www.frontiersin.org/articles/10.3389/fchem.2021.736457/full">"Molecular Determinants and Pharmacological Analysis for a Class of Competitive Non-transported Bicyclic Inhibitors of the Betaine/GABA Transporter BGT1"</a> (doi: 10.3389/fchem.2021.736457).</p> <p>The following data sets are available:</p> <ul> <li>Induced fit docking results of all mentioned compounds in the study: <br> ifd_hBGT1_occ_clustering_all_compounds.zip<br> </li> <li>MD simulations of bicyclo-GABA and compound 1 (100ns, 3 replica):<br> MD_simulation_bicyclo-GABA_run1.zip<br> MD_simulation_bicyclo-GABA_run2.zip<br> MD_simulation_bicyclo-GABA_run3.zip<br> MD_simulation_cmd1_run1.zip<br> MD_simulation_cmd1_run2.zip<br> MD_simulation_cmd1_run3.zip</li> </ul> <p>A detailed description of the methods is available in the aforementioned publication.</p> <p> </p> <p>The compound numbering in the uploaded files differs from the compound numbering in the mentioned study:</p> <p> </p> <p>study / upload</p> <p>bicyclo-GABA / cmd4</p> <p>1 / IIa</p> <p>2 /8-2</p> <p>3 / 8-3</p> <p>4a / 7-1</p> <p>4b / 7-2</p> <p>4c / 7-3</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Bicycle Tyre Data - Lateral Characteristics
<p>Measurement of lateral characteristics of bicycle tyres performed through indoor test-rig VeTyT (Department of Mechanical Engineering, Politecnico di Milano, Milan IT). The test-rig for bicycle tyres complies with the standard ISO 9001-2015.</p> <p>Specifically, we measured lateral force and self-aligning torque varying vertical load, inflation pressure, camber angles, for a batch of bicycle tyres. </p> <p> </p> <p><strong>Authors’ disclaimer</strong></p> <p><br> The data produced in this paper are not -and cannot be- related in any way on the quality of the products that have been tested. Actually, only one single tyre per type has been tested and the data have been acquired in a laboratory, which is not the real environment. Relevant factors defining the quality of tyres are not -and cannot be- addressed in this paper.</p> <p> </p>
Pennyfarthest Bicycle
So my professor challenged me to make something in a day. it took a bit longer what with me moving states but overall i think it was a fun project. it was refreshing to work on something other than a gun. I used photo reference for the entire thing, taking few liberties for added realism. will add an artstation link for the reference when i get there. Source: Objaverse 1.0 / Sketchfab
Delft Instrumented Bicycle Data and Videos
<p>This is the data collected and analyzed in the following paper:</p> <p>Kooijman, J. D. G.; Schwab, A. L. & Moore, J. K. Some Observations on Human Control of a Bicycle Proceedings of the ASME 2009 International Design and Engineering Technical Conferences & Computers and Information in Engineering Conference, 2009</p> <p>It is in the a form to use with this software:</p> <p>https://github.com/moorepants/DelftBicycleDataViewer</p>
Tony's Bicycle Shop (Street Art) #3DST27
Astoria, Queens, NY 36 Photos, ReCap 360 40.773084, -73.910759 ( http://goo.gl/vyyNmw ) Source: Objaverse 1.0 / Sketchfab
Wooden bicycle
ID no.: MNS/MS/342 - E Museum: Nowy Sącz District Museum https://muzea.malopolska.pl/en/objects-list/2455 Digitalisation: RDW MIC, Virtual Małopolska project Source: Objaverse 1.0 / Sketchfab
The effect of haptic feedback in the balance of a bicycle
<p>Roll_data_perturbation folder includes the data of all 20 participants. The signals are organized in structures and contain both pure measurement data (labeled data) and the outputs of the Finite Impulse Response Model (labeled black_box).</p>
1943 Albert Hoffman bicycle LSD sheet and tab
Classic 1943 Albert Hoffman bicycle acid LSD sheet and tab Didn't see anything similar on the website, so had to make this and might as well upload it since I like it. Source: Objaverse 1.0 / Sketchfab
An evolved artificial radical cyclase enables the construction of bicyclic terpenoid scaffolds via an H-atom transfer pathway
<p>Data underlying the figures/tables of the publication "An evolved artificial radical cyclase enables the construction of bicyclic terpenoid scaffolds via an H-atom transfer pathway" <em>Nat. Chem.</em> (2024). https://doi.org/10.1038/s41557-024-01562-5</p>
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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)
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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.