Find research datasets worth reusing

opencc-by-4.0Sep 2020View details →

MoTiV: a Dataset of European User Mobility for Behavioral-Data

Mobility is a system involving several stakeholders. Therefore, it is relevant to characterize mobility behavior and preferences in a detailed way, to enable nuanced decisions. Current paradigms rely mostly on time saving, proposing to users solutions that include the shortest path. Even though the value of travel time can be extended beyond travel duration, no dataset to characterize mobility and value of travel time from different perspectives exists. This creates a gap between novel mobility paradigms and the characterization of user mobility. To enable the mining of user mobility under these new paradigms, in this paper, we present the MoTiV (Mobility and Time Value) dataset, which contains data about travelers and their journeys, collected from a mobile application, called Woorti. Each trip contains multi-faceted information: from the transport mode, through its evaluation, to the positive/negative experience factors. We also present a use case, which compares corresponding legs with different transport modes, studying experience factors that negatively impact users. We conclude by discussing other application domains and research opportunities enabled by the dataset.

opencc-by-4.0Oct 2023View details →

Data for "The effects of weather and mobility on respiratory viruses dynamics before and during the COVID-19 pandemic in the USA and Canada".

Epidemiological and mobility data analysed in the paper "The effects of weather and mobility on respiratory viruses dynamics before and during the COVID-19 pandemic in the USA and Canada".

opencc-by-4.0Nov 2021View details →

Bicycle Mobility Data: Current Use and Future Potential. An International Survey of Domain Professionals

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. Read the full publication: <a href="https://doi.org/10.3390/data6110121">https://doi.org/10.3390/data6110121 </a>

opencc-by-4.0Jul 2024View details →

Data underlying the paper titled "Positron unveiling high mobility graphene stack interfaces in Li-ion cathodes"

The folder includes data regarding 4 figures shown in this paper.  FIG_1: Simulation structure of 6 layers of graphene bulk and slab (6C_Bulk.vasp, 6C_Slab.vasp), LiCoO2(LCO_336.vasp), ABA Graphite coating LiCoO2(G@LCO.vasp).  FIG_2: Raw data of band structure of Graphite coating LCO (Band_G@LCO_EIGENVAL), Density of States (DOS_G@LCO_DOSCAR) FIG_3: Calculated plane-averaged charge density difference of SW-G@LCO perpendicular to (001) plane at the equilibrium distance. G@LCO_CHGCAR, LCO_CHGCAR, C_CHGCAR are the CHGCAR for Graphitte coating LCO, LCO, graphite, respectively.  d24_diff.vasp is the charge difference d24_PACD.dat is the plane-averaged charge density difference.  FIG_4: posden indicates the positron density, while posvtot means positron potential. Data are named by their structure.

opencc-by-4.0May 2023View details →

Data for removal kinetics and breakthrough curves of stormwater vehicle-related mobile organic contaminants in geomedia-amended sand columns [Dataset]

This dataset describes the transport and removal of stormwater vehicle-related mobile organic contaminants in geomedia-amended sand columns. The experiments aimed at providing sustainable treatment options for relevant persistent, mobile and toxic (i.e., PMT substances) linked to vehicular traffic pollution. We assessed removal for 1H-benzotriazole, N'N-diphenylguanidine, and hexamethoxymethyl-melamine (PMT precursor) in batch and column experiments using pyrogenic carbonaceous adsorbents (e.g., GAC and biochar). Data contain kinetics batch experiments and breakthrough curves for the target contaminants.

openmit-licenseAug 2020View details →

COVID-19 Mobility Data Aggregator

Description This repository includes: 1) Data scraper of Google, Apple and Waze Mobility data 2) Preprocessed mobility reports in different formats 3) Merged mobility reports in summary files About data About <a href="https://www.google.com/covid19/mobility/">Google COVID-19 Community Mobility Reports</a> About <a href="https://www.apple.com/covid19/mobility">Apple COVID-19 Mobility Trends Reports</a> About <a href="https://www.waze.com/covid19">Waze COVID-19 local driving trends</a> Description of data files Google reports (located in google_reports directory): The raw report in ZIP format: Global_Mobility_Report.zip Data for the worldwide (only 1st level of subregions): mobility_report_countries (CSV and Excel formats available) Data for Brazil: mobility_report_brazil (CSV and Excel formats available) Data for Europe: mobility_report_europe (CSV and Excel formats available) Data for Asia + Africa: mobility_report_asia_africa (CSV and Excel formats available) Data for North and South America + Oceania (Brazil and US excluded): mobility_report_america_oceania (CSV and Excel formats available) Apple reports (located in apple_reports directory): Raw report: applemobilitytrends.csv Data for the worldwide: apple_mobility_report (Google Sheets, CSV and Excel formats available) Data for the US: apple_mobility_report_US (CSV and Excel formats available) Waze reports (located in waze_reports directory): Raw CSV files: Waze_Country-Level_Data.csv, Waze_City-Level_Data.csv Preprocessed report: waze_mobility (Google Sheets, CSV and Excel formats available) Summary reports (located in summary_reports directory) These are merged Apple and Google reports. Report by regions: summary_report_regions (CSV and Excel formats available) Report by countries: summary_report_countries (Google Sheets, CSV and Excel formats available) Report for the US: summary_report_US (CSV and Excel formats available) License See LICENSE.txt Credits If you use this dataset, please also cite the original data sources: 1. Google LLC "Google COVID-19 Community Mobility Reports". https://www.google.com/covid19/mobility/ Accessed: <date> 2. Apple Inc. "Apple COVID-19 Mobility Trends Reports". https://www.apple.com/covid19/mobility Accessed: <date> 3. Waze Ltd "Waze COVID-19 Impact Dashboard". https://www.waze.com/covid19 Accessed: <date>

opencc-by-4.0Oct 2020View details →

Data from: The impact of human mobility networks on the global spread of COVID-19

This is empirical dataset from the paper "The impact of human mobility networks on the global spread of COVID-19". Specifically, the dataset includes several files: (a) the COVID-19 network - an origin/destination matrix (i.e., "covid_network.csv"); (b) the common language network - edgelist format (i.e. "edge_list_comlang.csv"); (c) the same continent network - edgelist format (i.e., "edge_list_continent.csv"; (d) the contiguity network (i.e., "edge_list_contig.csv");  (e) the migration network - edgelist format (i.e., "edge_list_migration_in.csv"; (f) the tourism network - edgelist format (i.e., edge_list_tourism_in.csv"); (g) the list of nodes (countries) corresponding to files (b)-(e) (i.e., "nodes.csv"). Additionally, we uploaded the Rcode used in the paper (i.e. "code"), as a .pdf file format, the data source for the figures included in the paper (i.e., "covid_network_matrix.csv", "matrix_migration_out.csv", "matrix_tourism.csv" - Figure 1; "Fig_2_a_matrix_comlang.csv", Fig_2_b_matrix_contig.csv", "Fig_2_c_matrix_continent.csv" - Figure 2; "Fig_3.graphmlz - Figure 3; Fig_4.graphmlz - Figure 4) and the "global network of COVID-19 onset" (an individual-level data) (i.e., "global_covid_network.csv").  For details, please, see the Methods section of the paper: The impact of human mobility networks on the global spread of COVID-19 (Hancean, M.-G., Slavinec, M., Perc, M).

opencc-by-4.0Nov 2020View details →

I-BiDaaS - TID - Synthetic Mobility Data

This is a synthetic data stream based on real-time, cell network events. These events are picked up by the antennas that are closer to the mobile phone thus providing an approximate location of the device. Every transaction of a mobile phone generates one of those events. A transaction can be, for instance, placing or receiving a call, sending or receiving an SMS, asking for a specific URL in your mobile phone browser, or sending a text message or a data transaction from/to any mobile phone app. There are also some synchronization events like, for instance, turning your mobile phone on or off, or when switching between location area networks (relatively big geographical areas comprising several cell towers).

opencc-by-4.0Jul 2022View details →

Data for: Mobile impurities interacting with a few one-dimensional lattice bosons

Dataset for Mobile impurities interacting with a few one-dimensional lattice bosons (<a href="https://iopscience.iop.org/article/10.1088/1361-6455/acb51b">10.1088/1361-6455/acb51b</a>). It corresponds to exact diagonalization results for energies and bipolaron sizes. The files correspond to the following figures in the preprint: Fig. 1a:    Ep_UBB2.dat     Fig. 1b:    Ep_UBB4.dat     Fig. 1c:    Ep_UBB6.dat     Fig. 1d:    Ep_UBB8.dat      Fig. 2:      Ep_UBI50.dat Fig. 5a:    Ebp_UBB2.dat          Fig. 5b:    Ebp_UBB4.dat          Fig. 5c:    Ebp_UBB6.dat          Fig. 5d:    Ebp_UBB8.dat          Fig. 6:      Ebp_UBI50.dat      Fig. 7a:    rbp_UBB2.dat      Fig. 7b:    rbp_UBB4.dat      Fig. 7c:    rbp_UBB6.dat      Fig. 7d:    rbp_UBB8.dat      Fig. 8:      rbp_UBI50.dat

opencc-by-4.0Jul 2019View details →

Context-Aware Dataset: STS - South Tyrol Suggests IoT Mobile App Data

STS dataset was collected by a context-aware recommender system mobile app named as <a href="https://play.google.com/store/apps/details?id=it.unibz.sts.android&hl=en">"South Tyrol Suggests"</a>. The app provides context-aware recommendations for attractions, events, public services, restaurants, and much more based on the rating preferences and personality factors of users. Contextual variables includes  <ul> <li>distance: far away, near by</li> <li>time available: half day, one day, more than one day</li> <li>temperature: burning, hot, warm, cool, cold, freezing</li> <li>crowdedness: crowded, not crowded, empty</li> <li>knowledge of surroundings: new to area, returning visitor, citizen of the area</li> <li>season: spring, summer, autumn, winter</li> <li>budget: budget traveler, price for quality, high spender</li> <li>daytime: morning, noon, afternoon, evening, night</li> <li>weather: clear sky, sunny, cloudy, rainy, thunderstorm, snowing</li> <li>companion: alone, with friends/colleagues, with family, with girlfriend/boyfriend, with children</li> <li>mood: happy, sad, active, lazy weekday: weekday, weekend</li> <li>travel goal: visiting friends, business, religion, health care, social event, education, scenic/landscape, hedonistic/fun, activity/sport</li> <li>means of transport: no transportation means, a bicycle, a car, public transport</li> </ul> More details can be found here: 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">"Techniques for cold-starting context-aware mobile recommender systems for tourism."</a> Intelligenza Artificiale 8, no. 2 (2014): 129-143.

opencc-by-4.0Jul 2024View details →

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

This datalake comprises several datasets from 37 networked low cost air quality multisensors (30 mobile ENEA MONICA(tm) + 7 fixed) along with 3 (fixed) + 1 (mobile) reference stations 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. 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 pervasive, multi-site, multi-seasonal deployment in Portici, a densely populated small area city (4km2, 55k + inhabitants) located 7km south of Naples, Italy. 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. . Further description of sensors and reference instruments are reported in the accompanying paper (see citation request). The dataset can be used for  <ul> <li> advanced (remote/universal/in field) data driven calibration strategies test or development including machine learning models</li> <li>mobile opportunistic data fusion methods development</li> <li>geomatics and data assimilation models studies</li> </ul> as well as low cost sensor characterization performance studies.

opencc-by-4.0Sep 2024View details →

Data for: Machine learning for predicting environmental mobility based on retention behaviour

This repository contains the data and supplementary information for the paper: "Machine learning for predicting environmental mobility based on retention behaviour".

opencc-by-4.0Mar 2020View details →

National Severe Storms Laboratory Mobile Mesonet data files from Lapse-Rate

This dataset includes files from the National Severe Storms Laboratory mobile mesonets operated during the LAPSE-RATE field campaign. Daily files from each operation day are uploaded, along with a readme file explaining the format and processing. Version 2 Notes: The original version of this upload contained files with incorrect QC flags. While the core data is correct, the QC flags can be useful for determining specific areas of interest or problems. After identifying this issue, the files were reprocessed to include the correct QC flags and were uploaded to the archive as Version 2. Any questions should be directed to sean.waugh@noaa.gov.

dryad40/100

Data from: Arm waving in stylophoran echinoderms: three-dimensional mobility analysis illuminates cornute locomotion

The locomotion strategies of fossil invertebrates are typically interpreted on the basis of morphological descriptions. However, it has been shown that homologous structures with disparate morphologies in extant invertebrates do not necessarily correlate with differences in their locomotory capability. Here, we present a new methodology for analysing locomotion in fossil invertebrates with a rigid skeleton through an investigation of a cornute stylophoran, an extinct fossil echinoderm with enigmatic morphology that has made its mode of locomotion difficult to reconstruct. We determined the range of motion of a stylophoran arm based on digitized three-dimensional morphology of an early Ordovician form, Phyllocystis crassimarginata. Our analysis showed that efficient arm-forward epifaunal locomotion based on dorsoventral movements, as previously hypothesized for cornute stylophorans, was not possible for this taxon; locomotion driven primarily by lateral movement of the proximal aulacophore was more likely. 3D digital modelling provides an objective and rigorous methodology for illuminating the movement capabilities and locomotion strategies of fossil invertebrates.

opencc-zeroMay 2020View details →

opencc-by-4.0Dec 2020View details →

Dataset and Jupyter worksheet interpreting the (results from) small- and wide-angle scattering data from a series of boehmite/epoxy nanocomposites. Accompanies the publication "Competition of nanoparticle-induced mobilization and immobilization effects on segmental dynamics of an epoxy-based nanocomposite"

Dataset and Jupyter worksheet interpreting the (results from) small- and wide-angle scattering data from a series of boehmite/epoxy nanocomposites. Accompanies the publication "Competition of nanoparticle-induced mobilization and immobilization effects on segmental dynamics of an epoxy-based nanocomposite", by Paulina Szymoniak, Brian R. Pauw, Xintong Qu, and Andreas Schönhals. Datasets are in three-column ascii (processed and azimuthally averaged data) from a Xenocs NanoInXider SW instrument. Monte-Carlo analyses were performed using McSAS 1.3.1, other analyses are in the Python 3.7 worksheet. Graphics and result tables are output by the worksheet.

dryad40/100

Data from: Direct quantification of ion composition and mobility in organic mixed ionic-electronic conductors

Ion transport in organic mixed ionic-electronic conductors (OMIECs) is crucial due to its direct impact on device response time and fundamental operating mechanisms but are often assessed indirectly or rely on extra assumptions. Operando X-ray fluorescence (XRF) is a powerful, direct probe useful for elemental characterization of bulk OMIECs, and was employed to directly quantify ion composition and mobility in a model OMIEC, PEDOT:PSS, during device operation. The first cycle revealed slow electrowetting and cation-proton exchange. Subsequent cycles showed rapid response with minor cation fluctuation (~5%). Comparison with optical-tracked electrochromic fronts revealed a mesoscale structure dependent proton transport. The calculated effective ion mobility demonstrated thickness-dependent behavior, emphasizing an interfacial ion transport pathway with a higher mobile ion density. The decoupling of bulk and interfacial effects on ion mobility, and the decoupling of cation and proton transport contributes to our understanding of ion transport in conventional and emerging OMIEC-based devices, and has broader implications for ion transport in other ionic conductors writ large.

opencc-zeroMar 2024View details →

dryad40/100

Data for: Channel mobility and floodplain reworking across river planform morphologies

Source-to-sink transfer of sediment and organic carbon (OC) is regulated by river mobility. Quantifying trends in river mobility is, however, challenging due to diverse planform morphologies (e.g., meandering, braided) and measurement methods. Here, we utilize a state-of-the-art remote-sensing method applicable to all planform morphologies to quantify the mobility timescales of 80 rivers worldwide. Results show that, across the continuum from meandering to braided rivers, there is a systematic reduction in timescale of channel mobility and—to a lesser extent—the timescale of floodplain reworking. This leads to an overall decrease in the efficiency at which braided river channels rework old floodplain material compared to their meandering counterparts. Reduced reworking efficiency of braided channels stems from their relatively smaller channel-belt areas relative to their channel area. Results suggest that river-mobility timescales can help us characterize sediment and OC storage and transit times from remote sensing.

opencc-zeroApr 2024View details →

opencc-by-4.0Mar 2022View details →

A hierarchical graph-based model for mobility data representation and analysis

Hierarchical representations of transportation networks should provide a better understanding of mobility patterns and the underlying structures at various abstraction levels. A hierarchical graph-based model allows representing moving objects and trajectories according to multiple spatial, temporal and semantic scales. The latter model is implemented here in a Neo4j graph database (version 4.4.0) and experimented with historical maritime data covering Brittany Bay in France.