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363 results for “travel”
Data from: Climate impacts on the ocean are making the Sustainable Development Goals a moving target traveling away from us
1. Climate change is impacting marine ecosystems and their goods and services in diverse ways, which can directly hinder our ability to achieve the Sustainable Development Goals, set out under the 2030 Agenda for Sustainable Development. 2. Through expert elicitation and a literature review, we find that most climate change effects have a wide variety of negative consequences across marine ecosystem services, though most studies have highlighted impacts from warming and consequences to marine species. 3. Climate change is expected to negatively influence marine ecosystem services through global stressors – such as ocean warming and acidification – but also by amplifying local and regional stressors such as freshwater runoff and pollution load. 4. Experts indicated that all Sustainable Development Goals would be overwhelmingly negatively affected by these climate impacts to marine ecosystem services, with eliminating hunger being among the most directly negatively affected Sustainable Development Goal. 5. Despite these challenges, the Sustainable Development Goals aiming to transform our consumption and production practices and develop clean energy systems are found to be least affected by marine climate impacts. These findings represent a strategic point of entry for countries to achieve sustainable development, given that these two goals are relatively robust to climate impacts and that they are important pre-requisite for other Sustainable Development Goals. 6. Our results suggest that climate change impacts on marine ecosystems are set to make the Sustainable Development Goals a moving target traveling away from us. Effective and urgent action towards sustainable development, including mitigating and adapting to climate impacts on marine systems are important to achieve the Sustainable Development Goals, but the longer this action stalls the more distant these goals will become.
Data from: The travel speeds of large animals are limited by their heat-dissipation capacities
<div> <h2>Purpose</h2> <p>This is an extension of the travel speed dataset published by Dyer et al. 2023 in PLOS Biology that includes additional metadata. Data follow the same aggregation to species level by grouping records across unique combinations of study and species while preserving variation across important categorical covariates (listed below). </p> </div> <div> <h2>Scripts</h2> <ul> <li><code>scripts/modelling/fit_model.R</code>: Fits mechanistic locomotion models to aggregated data using RStan. Plots predictions and outputs parameter estimates of competing models.</li> </ul> </div> <div> <h2>Data Columns</h2> <p>Each heading below describes a column of the travel speed dataset.</p> <p><strong>move_speed_ref</strong>: Unique reference number for each study reporting animal travel speed. The corresponding references are contained within the supplementary reference list file.</p> <p><strong>move_mass_ref</strong>: Unique reference number for each study reporting animal body mass. The corresponding references are contained within the supplementary reference list file.</p> <p><strong>scientific_name</strong>: Scientific name of the species according to the taxonomy of the Global Biodiversity Information Facility (accessed via GBIF.org during 2022). A small number of studies that report only the genus name or common name of the species are reported in our dataset as <em>Genus sp.</em> (e.g. <em>Gazella sp.</em>). One study, which estimated the travel speed and body mass of an unknown species of mouse via camera traps, is reported in our dataset as <em>Amazon mouse sp.</em></p> <p><strong>move_movement_mode</strong>: Categorical value indicating whether the reported travel speed corresponds to an animal engaged in flying, running, or swimming. This allows species to be accommodated that are capable of multi-modal locomotion (e.g. northern elephant seal, <em>Mirounga angustirostris</em>).</p> <p><strong>taxon_group</strong>: Categorical value indicating membership to one of eight animal groups (amphibian, arthropod, cnidarian, bird, fish, mammal, mollusc, reptile).</p> <p><strong>thermo_reg</strong>: Categorical value indicating thermoregulatory strategy, i.e. the contribution of metabolic heat production to the maintenance of core body temperature during rest:<br>- <strong>ectotherm</strong>: negligible contribution, body temperature matches ambient temperature<br>- <strong>mesotherm</strong>: weak contribution with partial thermal stability, as in tunas, lamnid sharks, and leatherback sea turtles<br>- <strong>endotherm</strong>: strong contribution with metabolic stability even when ambient temperatures are significantly below body temperature</p> <p><strong>move_medium</strong>: Categorical value indicating whether the animal’s travel speed was measured during locomotion within the terrestrial realm (air) or aquatic realm (water).</p> <p><strong>move_speed_method</strong>: Categorical value indicating whether travel speed was estimated directly (i.e. instantaneously via direct observation in real-time, animal-attached speedometer, or video recording) or indirectly from higher-resolution telemetry data (i.e. from changes in an animal’s spatial coordinates at intervals < 30 minutes apart).</p> <p><strong>move_study_condition</strong>: Categorical value indicating whether travel speed was estimated under natural field conditions or under a controlled laboratory setting such as within an aquarium or mesocosm. Travel speeds of the smallest animals (e.g. arthropods) can only feasibly be estimated within a controlled setting.</p> <p><strong>move_avgspeed_value</strong>: Categorical value indicating whether average travel speed was reported as a mean or median within the original study.</p> <p><strong>move_bodymass_kg</strong>: Continuous value indicating the average adult body mass (units: kilograms) of the species. In cases where the study’s reference numbers <code>move_speed_ref</code> and <code>move_mass_ref</code> differ, we referred to secondary literature sources to assign the average adult body mass of the species. In cases where only body length was given, we used published allometric equations to estimate the wet body mass.</p> <p><strong>move_avgspeed_ms</strong>: Continuous value indicating the average (geometric mean) travel speed (units: metres per second) of the species reported within the study.</p> <p><strong>move_speed_source</strong>: Categorical value indicating the sampling level at which travel speed measurements were reported within the study.<br>- <code>indv</code>: travel speed reported from an individual animal. - <code>avgs</code>: travel speed reported as an average (mean or median) across multiple individuals from the same species.</p> <p><strong>move_individuals_n</strong>: Integer value indicating the number of individual animals from which travel speed measurements were obtained within the study.</p> <p><strong>move_measurements_n</strong>: Integer value indicating the number of travel speed measurements taken across individuals within the study. This value can exceed <code>move_individuals_n</code> when repeated measurements were made from the same individuals.</p> <p><strong>move_ygeo_min</strong>: Continuous value indicating the minimum reported latitude (decimal degrees) of the study location(s).</p> <p><strong>move_ygeo_max</strong>: Continuous value indicating the maximum reported latitude (decimal degrees) of the study location(s).</p> <p><strong>move_xgeo_min</strong>: Continuous value indicating the minimum reported longitude (decimal degrees) of the study location(s).</p> <p><strong>move_xgeo_max</strong>: Continuous value indicating the maximum reported longitude (decimal degrees) of the study location(s).</p> <p><strong>move_date_min</strong>: Earliest reported date of data collection within the study (format: YYYY-MM-DD).</p> <p><strong>move_date_max</strong>: Latest reported date of data collection within the study (format: YYYY-MM-DD).</p> <p><strong>move_study_country</strong>: Country in which the study was conducted.</p> <p><strong>move_study_location</strong>: A description of the study location.</p> </div> <div> <h2>Version Notes</h2> <p>This updated version of the travel speed dataset extends the aggregated dataset published in <br>Dyer et al. 2023, PLOS Biology<strong> </strong>through the inclusion of additional metadata fields.<br>Metadata follow the same aggregation to species level by grouping records across unique combinations of study and species while preserving variation across important categorical covariates (listed above).</p> <div> <h3>Key Updates</h3> <ul> <li><strong>Added spatial metadata</strong>: minimum and maximum latitude/longitude, study country, and study location.</li> <li><strong>Added temporal metadata</strong>: earliest and latest reported dates of data collection (YYYY-MM-DD).</li> <li><strong>Refined sampling information</strong>: number of individuals (<code>move_individuals_n</code>) and number of speed measurements (<code>move_measurements_n</code>) are now reported, where available.</li> </ul> </div> </div>
Internal rotation and buoyancy travel time of 60 gamma Doradus stars from uninterrupted TESS light curves spanning 352 days
<p>Description:<br> Electronic versions of Table A.1 and A.2 from the Appendix of<br> Garcia et al. (2022b), as well as all analysed g-mode period-spacing<br> patterns from this work.</p> <p>Abstract:<br> Context. Gamma Doradus (hereafter gamma Dor) stars are gravity-mode<br> pulsators whose periods carry information about the internal structure of<br> the star. These periods are especially sensitive to the internal rotation<br> and chemical mixing, two processes that are currently not well constrained<br> in the theory of stellar evolution.<br> Aims. We aim to identify the pulsation modes and deduce the internal<br> rotation and buoyancy travel time for 106 gamma Dor stars observed<br> by the TESS mission in its southern continuous viewing zone (hereafter<br> S-CVZ). We rely on 140 previously detected period-spacing patterns, that is,<br> series of (near-)consecutive pulsation mode periods.<br> Methods. We used the asymptotic expression to compute gravity-mode<br> frequencies for ranges of the rotation rate and buoyancy travel time that<br> cover the physical range in γ Dor stars. Those frequencies were fitted to<br> the observed period-spacing patterns by minimizing a custom cost function.<br> The effects of rotation were evaluated using the traditional approximation<br> of rotation, using the stellar pulsation code GYRE.<br> Results. We obtained the pulsation mode identification, internal rotation<br> and buoyancy travel time for 60 TESS gamma Dor stars. For the remaining 46<br> targets, the detected patterns are either too short or contained too many<br> missing modes for unambiguous mode identification, and longer light curves<br> are required. For the successfully analysed stars, we found that<br> period-spacing patterns from 1-yr long TESS light curves can constrain the<br> internal rotation and buoyancy travel time to a precision of 0.03 d^{−1} and<br> 400s, respectively, which is about half as precise as literature results<br> based on 4-yr Kepler light curves of gamma Dor stars.</p>
Detour Data and Traveler Survey
<p>Detour Survey and Count Data</p>
The Electric Vehicle Travelling Salesman Problem on Digital Elevation Models for Traffic-Aware Urban Logistics Supplementary Material
<p>These files correspond to the supplementary material of the article <em>The Electric Vehicle Travelling Salesman Problem on Digital Elevation Models for Traffic-Aware Urban Logistics</em>. </p> <p><strong>Code</strong></p> <ul> <li><strong>algorithm.py</strong> corresponds to an implementation of the algorithm developed in the paper to solve the EV-TSP for the city of Madrid. It takes as input a list of nodes from the graph of Madrid city <strong>madrid_elevation_energy.pckl</strong> and the output consists of an ordered list of all the nodes representing the solution to the TSP.</li> <li><strong>bellmanFord.py</strong> is a Python implementation of the Bellman-Ford algorithm. </li> <li><strong>evaluation.py</strong> is the script that offers the evaluation of the algorithm offered in Tables 1 and 2 in the paper.</li> <li><strong>neuralNetworkTraining.py</strong> is the script used to train and save the Neural Network model using the data generated by <strong>simulation.py</strong>.</li> <li><strong>nn_model_predictor.py</strong> is a script where the model trained in <strong>neuralNetworkTraining.py</strong> can be used to generate predictions.</li> <li><strong>simulation.py</strong> is the script that simulated the routes through the months of October and November 2022 using the data in <strong>snapshots_2022.zip</strong>. It generates the routes in <strong>simulationOctober.csv</strong> and <strong>simulationNovember.csv</strong></li> <li><strong>twoOptNearestNeighnors.py</strong> is a Pyhton implementation of the 2-Opt algorithm that uses Nearest Neighbors to generate the initial tour.</li> </ul> <p><strong>Files</strong></p> <ul> <li><strong>Madrid{5,10,15}.pkl</strong> are the test instances for the city of Madrid. Correspond to Python list of list. Each list is a set of stops to visit in the city graph of Madrid (<strong>madrid_elevation_energy.pckl</strong>) </li> <li><strong>energy_estimation_full.h5</strong> is a Keras model trained using <strong>nn_model_predictor.py</strong> to estimate the energy.</li> <li><strong>scaler_full.pkl</strong> is the scaler needed to use the <strong>energy_estimation_full.h5</strong> model.</li> <li><strong>simulation{October, November}.pkl</strong> are the routes generated for each month using <strong>simulation.py</strong>.</li> <li><strong>snapshots_2022.zip</strong> are the traffic data for the months of October and November 2022</li> </ul>
Urban nature visitation, accessibility, and impact of travel distance for sustainable cities - Data
<p>Data used in the analysis for: Talal, M.L., Gruntman, M. Urban nature visitation, accessibility, and impact of travel distance for sustainable cities. <i>Sci Rep</i> <strong>13</strong>, 17808 (2023). https://doi.org/10.1038/s41598-023-44861-6</p><p>The data was collected in a web-based survey of Tel Aviv-Yafo residents between March 21, 2021 and May 5, 2021. It includes information for 421 visitors of urban nature sites, such as visit frequency per year before and during COVID-19 and distance from home (km) category. </p>
Statistics of electric vehicle travel time in a certain city
<p>This dataset contains data on the charging time of electric vehicles for a typical month in a certain city. The types of electric vehicles include: buses, private passenger cars, ride hailing vehicles, logistics vehicles, and rental passenger cars. The activity area is divided into: office area, industrial area, residential area, and commercial area. The dataset takes one hour as the statistical cycle to calculate the charging frequency of electric vehicles of various types and regions during a certain period of time.</p>
Study to Evaluate Safety and Efficacy of Rifamycin SV Multi-Matrix System (MMX) for the Treatment of Traveler's Diarrhea (TD)
ClinicalTrials.gov study NCT01142089. IPD Sharing: Not stated. Countries: 2. Publications: 0.
Safety and Immunogenicity Study of Traveler's Diarrhea Vaccine Patch
ClinicalTrials.gov study NCT01067781. IPD Sharing: Not stated. Countries: 1. Publications: 0.
A Proof of Concept Study to Evaluate the Effects of Tasimelteon and Placebo in Travelers With Jet Lag Disorder
ClinicalTrials.gov study NCT03291041. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Traveler's Diarrhea (TD) Automated Process
ClinicalTrials.gov study NCT00751777. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Study Comparing Rifaximin With Xifaxan 200 mg in Traveler's Diarrhea
ClinicalTrials.gov study NCT02498418. IPD Sharing: NO. Countries: 1. Publications: 0.
A Study Comparing Two Rifaximin Tablets in Patients With Travelers' Diarrhea.
ClinicalTrials.gov study NCT02920242. IPD Sharing: Not stated. Countries: 1. Publications: 0.
A Comprehensive Travel Health Education for Tour Guides in Bali, Indonesia
ClinicalTrials.gov study NCT04961983. IPD Sharing: NO. Countries: 0. Publications: 47.
Travelling in Patients With Sleep Related Breathing Disorders
ClinicalTrials.gov study NCT02636478. IPD Sharing: Not stated. Countries: 0. Publications: 2.
Rifamycin SV-MMX® Tablets Versus Ciprofloxacin Capsules in Acute Traveller's Diarrhoea
ClinicalTrials.gov study NCT01208922. IPD Sharing: Not stated. Countries: 3. Publications: 0.
Do Superfast Broadband and Tailored Interventions Improve Use of E-health and Reduce Health Related Travel?
ClinicalTrials.gov study NCT02355808. IPD Sharing: Not stated. Countries: 0. Publications: 1.
Transmeridian Travel in Elite Female Athletes
ClinicalTrials.gov study NCT06920225. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Assessment of Safety of Air Travel in Patients With Birt-Hogg-Dube Syndrome
ClinicalTrials.gov study NCT03040115. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Employee airline travel preferences survey data, UC Davis GreenFLY project
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