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181 results for “outdoor”
Outdoor mesocosm study evaluating how mass, NaCl tolerance, and pesticide tolerance affect oxidative stress biomarkers (CAT, SOD, GR, GPx, TBARS) in larval wood frogs (Rana sylvatica) exposed to baseline and NaCl-contaminated conditions, 2019
Biomarkers of oxidative stress can aid in wildlife monitoring by allowing conservationists to detect sublethal environmental shifts. However, interpretation of stress responses can be complicated by multiple interacting factors (e.g., individual development, evolved physiological tolerance to stressors) which alter biomarker expression. Here, we investigated how individual ontogenetic traits and population-level tolerance traits influence oxidative stress responses under baseline and contaminated environmental conditions. For our model contaminant, we used NaCl (common freshwater contaminant due to factors such as coastal flooding, irrigation, airborne salt circulation, drought, runoff from road deicing salts). For our model wildlife populations, we used larval wood frogs (Rana sylvatica) from six noninteracting populations known to vary in two population-level tolerance traits: NaCl tolerance (calculated as average time to death from lethal NaCl exposure) and pesticide tolerance (determined by proxy of distance to agriculture - a consistent and highly repeatable relationship). At an outdoor research facility, R. sylvatica tadpoles were exposed to either baseline conditions (0 g/L NaCl added) or NaCl-contaminated conditions (1 g/L NaCl added for 21 days, then reduced to 0.5 g/L NaCl). Exposures were conducted in individual units with 40 replicates per population for each treatment. The experiment was terminated per individual to capture the full term of larval development (Developmental stage: Gosner stage 36), lasting between 33-48 days. For each individual, we measured mass, Snout-Vent-Length, and developmental stage before processing for biomarker expression. Individual homogenates were assayed for oxidative stress biomarkers superoxide dismutase (SOD; responsible for Reactive Oxygen Species capture and peroxide production), glutathione peroxidase (GPx; responsible for high-affinity peroxide reduction), catalase (CAT; responsible for low-affinity peroxide reducti
GRIME AI Water Segmentation Model for the USGS Monitoring Site at Kearney Outdoor Learning Area, NE, 2024-2024
Ground-based observations from fixed-mount cameras have the potential to fill an important role in environmental sensing, including direct measurement of water levels and qualitative observation of ecohydrological research sites. All of this is theoretically possible for anyone who can install a trail camera. Easy acquisition of ground-based imagery has resulted in millions of environmental images stored, some of which are public data, and many of which contain information that has yet to be used for scientific purposes. The goal of this project was to develop and document key image processing and machine learning workflows, primarily related to semi-automated image labeling, to increase the use and value of existing and emerging archives of imagery that is relevant to ecohydrological processes. This data package includes imagery, annotation files, water segmentation model and model performance plots, and model test results (overlay images and masks) for the USGS Monitoring Site at Kearney Outdoor Learning Area, NE, 2024-2024. All imagery was acquired from the USGS Hydrologic Imagery Visualization and Information System (HIVIS; see https://apps.usgs.gov/hivis/camera/NE_Kearney_Outdoor_Learning_Area for this specific data set) and/or the National Imagery Management System (NIMS) API. Water segmentation models were created by tuning the open-source Segment Anything Model 2 (SAM2, https://github.com/facebookresearch/sam2) using images that were annotated by team members on this project. The models were trained on the "water" annotations, but annotation files may include additional labels, such as "snow", "sky", and "unknown". Image annotation was done in Computer Vision Annotation Tool (CVAT) and exported in COCO format (.json). All model training and testing was completed in GaugeCam Remote Image Manager Educational Artificial Intelligence (GRIME AI, https://gaugecam.org/) software (Version: Beta 16). Model performance plots were automatically generated during this pr
Drone onboard multi-modal sensor dataset for complex outdoor scenarios
<p>The Data acquisition missions were designed and executed using DJI Pilot 2’s flight route planning feature. The missions encompassed five distinct geometric patterns: 1. triangular, 2. circular, 3. rectangular, 4. linear, and 5. multi-dimensional. Each mission was configured as a waypoint flight path, allowing precise customization of parameters such as altitude, speed, and turning angle for each waypoint. The dataset consists of 3D space flight data such as take-off, landing and varying altitude to introduce the z-axis changes. It must be noted that data was logged at a frequency of 10 Hz.</p> <p>To ensure consistency within the data, identical parameters were maintained across all data acquisition missions. The dataset comprises 20 distinct flights, with each flight path repeated multiple times, resulting in approximately 30 minutes of flight time per mission. The dataset is structured as time-series data, with each flight uniquely identified by a flight number and corresponding timestamp. The drone's spatial position is represented by the variables <strong>position_x, position_y, position_z </strong>while its orientation is captured by the variables <strong>orientation_x, orientation_y, orientation_z, orientation_w</strong>. Additionally, the drone's velocity and angular velocity are represented by the variables <strong>velocity_x, velocity_y, velocity_z, angular_x, angular_y, angular_z </strong>respectively. The linear acceleration is described by the variables <strong>linear_acceleration_x, linear_acceleration_y, linear_acceleration_z</strong>. The dataset also includes environmental data such as <strong>wind_speed, wind_angle </strong>using the TriSonica Mini Wind and Weather Sensor as well as information regarding the drone's battery status, including <strong>battery_voltage, battery_current.</strong></p> <p><strong>Data Acquisition Paths: <a href="https://ucy-my.sharepoint.com/:i:/g/personal/ygrigo01_ucy_ac_cy/EYAgdcLGCWxPloO1NMnsF-8Btf390Kmx854IuDe9R3E1ig?e=3Trbuk">Data acquisition paths</a></strong></p> <p>The dataset includes labels for various operational states of the drone, such as IDLE_HOVER, ASCEND, TURN, HMSL and DESCEND. These labels can be utilized to classify the drone's current activity. Moreover, the annotated dataset can be applied in multi-task learning to predict the drone's trajectory.</p> <p>The DJI Matrice 300 RTK is utilized as the primary platform for data acquisition, leveraging its compatibility with onboard development kits to facilitate the extraction of data from its integrated sensors and flight controller. To execute the developed software the NVIDIA Jetson Xavier NX serves as the embedded computing device. Utilizing the Onboard software development kit the Jetson Xavier NX enables real-time access and processing of data from the drone's sensors and flight controller.</p>
Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model: Morphed hourly outdoor temperatures for Jyvaskyla for 2030 and 2050
<p>******************* Please view the README.txt or README.md file for detailed documentation of data. ********************</p> <p>Title: Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model: Morphed hourly outdoor temperatures for Jyväskylä for 2030 and 2050</p> <p>Date of release: 25/11/2020</p> <p>Identifier: 10.5281/zenodo.4275759</p> <p>Permalink: http://dx.doi.org/10.5281/zenodo.4275759</p> <p>Associated publication: Hietaharju, P.; Louis, J.-N.; Pulkkinen, J.; Ruusunen, M. Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model, <strong><em>Under Review</em></strong>, 2020.</p> <p>Suggested citation: Please reference the associated publication above when using any datasets or materials described in the README.txt and README.md files.</p> <p><br> Contact information: Jari Pulkkinen, University of Oulu, Oulu, Finland, jari.pulkkinen@oulu.fi; Jean-Nicolas Louis, University of Oulu, Oulu, Finland, jean-nicolas.louis@oulu.fi<br> </p> <p>Dates of data: 2030, 2050</p> <p>Type of data: Outdoor Temperature</p> <p>Geographic location: Jyväskylä</p> <p>Time resolution: hourly, full year</p> <p>Format: All data is stored in .csv files</p> <p>Number of files: 1 .zip --> 50 files + README.txt + README.md</p> <p>This directory contains the following datasets: A summary of all the files has been compiled and stored in the "README.txt" and "README.md" files</p> <p> </p> <p>Notifications:</p> <p>Contains modified Copernicus Climate Change Service (C3S) information [2018] and modified Finnish Meteorological Institute [2017,2019] information from etsin.fairdata.fi and from Open data repository (https://en.ilmatieteenlaitos.fi/open-data).</p> <p><br> Contains modified Climate One Building information [2019] (reference Lawrie L.K. and Crawley D.B. 2019) and Test Reference Year 2012 (TRY2012) information from Jylhä et al. [2011] and Jylhä et al. [2015] (Energy demand for the heating and cooling of residential houses in Finland in a changing climate).</p> <p>Contains modified Ruosteenoja et al. [2016] information.</p> <p>Other data and information sources are described in README.txt, README.md, references and on the associated publication.</p>
Dataset _ Influence of the seasonal variation of environmental conditions on biogas upgrading in an outdoors pilot scale high rate algal pond
<p>This is the dataset used for the publication of the journal article title<em> “</em><strong>Influence of the seasonal variation of environmental conditions on biogas upgrading in an outdoors pilot scale high rate algal pond”. </strong>In this dataset there is all the information collected in the experimentation process.</p>
Dataset _ Seasonal variation of biogas upgrading coupled with digestate treatment in an outdoors pilot scale algal-bacterial photobioreactor
<p>This is the dataset used for the publication of the journal article title<em> “</em><strong>Seasonal variation of biogas upgrading coupled with digestate treatment in an outdoors pilot scale algal-bacterial photobioreactor</strong><strong>”. </strong>In this dataset there is all the information collected in the experimentation process.</p>
Outdoor monitoring data of an Insolight B-series module - CPV sub-module
<p>Dataset from the outdoor characterization of a B Series module from Insolight at the rooftop of the Instituto de Energía Solar - Universidad Politécnica de Madrid. It was the basis of its power rating at CSTC and has informed several scientific and technical articles like the oral presentation by Gaël Nardin et al. "Towards Industrialization of Planar Microtracking Photovoltaic Panels" at the 15th International Conference on Concentrator Photovoltaics (CPV-15), held between 25 and 27 March of 2019 in Fes, Morocco. </p> <p><strong>Monitoring campaign:</strong></p> <ul> <li>Location: 40.453°N, -3.727°E. <a href="https://www.google.com/maps/place/40%C2%B027'11.6%22N+3%C2%B043'37.3%22W/@40.453215,-3.7275722,142m/data=!3m2!1e3!4b1!4m13!1m6!3m5!1s0x0:0xc636231f90c3bbeb!2sInstituto+de+Energ%C3%ADa+Solar!8m2!3d40.4531766!4d-3.7269107!3m5!1s0x0:0x0!7e2!8m2!3d40.4532142!4d-3.7270248">Instituto de Energía Solar</a>, Universidad Politécnica de Madrid. 28040 Madrid, Spain.</li> <li>Starting date: 21 November 2018</li> <li>End date: 14 December 2018</li> </ul> <p><strong>Description of data files:</strong></p> <ul> <li><strong>Data files format:</strong> tab-separated text file; headers in first row.</li> <li><strong>I-V parameters</strong>: single file "Insolight Preseries outdoor monitoring - IES rooftop - UPM.txt". Headers: <ul> <li>Date (DD/MM/YYYY) </li> <li>Time (HH:MM:SS): time in CET / UTC+1</li> <li>Pmp (W): peak power</li> <li>Vmp (V): voltage at maximum power point</li> <li>Imp (A): current at maximum power point</li> <li>Isc (A): short-circuit current</li> <li>Voc (V): open-circuit voltage</li> <li>FF: fill factor</li> <li>Tair (°C): ambient temperature </li> <li>Tlens (°C): temperature at the aperture plane</li> <li>GNI (W/m<sup>2</sup>): global normal irradiance at the aperture plane as measured with a pyranometer. Spectral range: 305 – 2800 nm.</li> </ul> </li> <li><strong>Weather data</strong>: daily files "geonica2018_**_**.txt". Headers: <ul> <li>yyyy/mm/dd: date</li> <li>hh:mm: time in GMT/UTC</li> <li>V_Viento (m/s): wind speed</li> <li>D_Viento (°N): wind direction</li> <li>Temp_Air(°C): ambient temperature</li> <li>Rad_Dir(W/m<sup>2</sup>): direct normal irradiance as measured by a Normal Incidence Pyrheliometer from Eppley. Spectral Range: 250-3000 nm. Field of view: 5°</li> <li>Ele_Sol (°): solar tilt</li> <li>Ori_Sol (°N): solar position azimuth</li> <li>Top (W/m<sup>2</sup>): direct normal irradiance as measured by a top component cell of a lattice-matched III-V triple-junction cell in the <a href="http://solaraddedvalue.com/en/category/products/spectro-heliometer/">ICU-3J35</a> spectroheliometer from Solar Added Value. Spectral range: 300 - 680 nm. Field of view: 5.7º</li> <li>Mid (W/m<sup>2</sup>): direct normal irradiance as measured by a middle component cell of a lattice-matched III-V triple-junction cell in the <a href="http://solaraddedvalue.com/en/category/products/spectro-heliometer/">ICU-3J35</a> spectroheliometer from Solar Added Value. Spectral range: 680 - 900 nm. Field of view: 5.7º</li> <li>Bot (W/m<sup>2</sup>): direct normal irradiance as measured by a bottom component cell of a lattice-matched III-V triple-junction cell in the <a href="http://solaraddedvalue.com/en/category/products/spectro-heliometer/">ICU-3J35</a> spectroheliometer from Solar Added Value. Spectral range: 900 - 1800 nm. Field of view: 5.7º</li> <li>Cal_Top: n/a</li> <li>Cal_Mid: n/a</li> <li>Cal_Bot: n/a</li> <li>Pres_Aire: n/a</li> </ul> </li> </ul>
iSCAPE Outdoor Sensor Deployment Data
<p><strong>Dataset Description</strong></p> <p>This dataset contains all the deployment data using low cost sensors during the iSCAPE project. The dataset is divided in a series of deployments, each of them described on a yaml file with the test name. Each csv file contains time series data of each experiment, and the yaml files contain the lists of devices used in each test. The tests are described in the comment of the yaml file, and are meant to be self explanatory. Two different types of tests are herein presented:</p> <ol> <li>Intervention monitoring tests (Guildford and Surrey). Two intervention monitoring results were conducted in the sites of Surrey (UoS) Living Lab and Hasselt (UH) Living Lab. The intervention in Surrey aimed at characterising the behaviour of green infrastructure and the effect on the pollutants dispersion next to traffic conditions. Two different sets of two stations were delivered and deployed, one set in the vicinity of Stoke Park, and the other in the vicinity of Sutherland Memorial Park (both in Guildford - UK). In the case of Hasselt, two Living Lab Stations were deployed. The first one was used to assess pollutant concentrations in the <em>Bassischool Kuringen</em> in Hasselt. The other station was deployed near the University of Hasselt.</li> <li>Sensor Calibration tests (CSIC, Dublin and Bologna). The tests conducted in Bologna (by UNIBO, 2018), Dublin (by UCD, 2019) and Barcelona (by IAAC, 2019) were intended as an assessment of the sensor technology in an outdoor environment scenario, by co-locating the iSCAPE LLSs with reference instrumentation.</li> </ol> <p>A complete description of these datasets and the result of their analysis is shown in D7.8 of iSCAPE which can be found in this url: <a href="https://www.iscapeproject.eu/results/">https://www.iscapeproject.eu/results/</a>.</p> <p><strong>Sensors</strong></p> <p>The sensors used are herein referred as Citizen Kits or Smart Citizen Kits, and the Living Lab Station or Smart Citizen Station. These are a set of modular hardware components that feature a selection of low cost sensors for environmental monitoring listed below. The Smart Citizen Station is meant to expand the capabilities of the Smart Citizen Kit, aiming to measure pollutants with more advanced sensors. The hardware is licensed under <a href="https://www.ohwr.org/licenses/cern-ohl/license_versions/v1.2">CERN Open Hardware License V1.2</a> and is fully described in the HardwareX Open Access publication: <a href="https://doi.org/10.1016/j.ohx.2019.e00070">https://doi.org/10.1016/j.ohx.2019.e00070</a>. The sensor documentation can be found at <a href="https://docs.smartcitizen.me">https://docs.smartcitizen.me</a> and with this DOI at Zenodo: <a href="https://doi.org/10.5281/zenodo.2555029">https://doi.org/10.5281/zenodo.2555029</a>.</p> <p>In the list below, the different sensors for the Citizen Kits are detailed, and their [CHANNELS] in the csv files above linked.</p> <ul> <li>Air temperature (ºC): Sensirion SHT-31 [TEMP]</li> <li>Relative Humidity (%rh): Sensirion SHT-31 [HUM]</li> <li>Noise level (dBA): Invensense ICS-434342 [NOISE_A]</li> <li>Ambient light (lux): Rohm BH1721FVC [LIGHT]</li> <li>Barometric pressure (kPa): NXP MPL3115A26 [PRESS]</li> <li>Particulate Matter PM 1 / 2.5 / 10 (µg/m3) Planttower PMS 5003 [EXT_PM_1,EXT_PM_25,EXT_PM_10]</li> </ul> <p>In the list below, the different sensors for the Citizen Kits are detailed, and their [CHANNELS] in the csv files above linked.</p> <ul> <li>Air Temperature (ºC) Sensirion SHT-31 [TEMP]</li> <li>Relative Humidity (% REL) Sensirion SHT-31 [HUM]</li> <li>Noise Level (dBA) Invensense ICS-434342 [NOISE_A]</li> <li>Ambient Light (Lux) Rohm BH1721FVC [LIGHT]</li> <li>Barometric pressure and AMSL (Pa and Meters) NXP MPL3115A26 [PRESS]</li> <li>Carbon Monoxide (µg/m3 (Periodic Baseline Calibration Required) SGX MICS-4514 [NA]</li> <li>Nitrogen Dioxide (µg/m3 (Periodic Baseline Calibration Required) SGX MICS-4514 [NA]</li> <li>Carbon Monoxide (ppm) Alphasense CO-B4 [GB_1W, GB_1A, final calculated valueCO_DELTAS_OVL_X-XX-XX - all the same]</li> <li>Nitrogen Dioxide (ppb) Alphasense NO2-B43F [GB_2W, GB_2A, final calculated value NO2_DELTAS_OVL_0-30-50 or NO2_DELTAS_OVL_0-5-50] </li> <li>Ozone (ppb) Alphasense OX-B431 [GB_3W, GB_3A, final value O3_DELTAS_OVL_0-30-50 or O3_DELTAS_OVL_0-5-50]</li> <li>Gases Board Temperature (ºC) Sensirion SHT-31 [GB_TEMP] or [EXT_TEMP]</li> <li>Gases Board Rel. Humidity (% REL) Sensirion SHT-31 [GB_HUM] or [EXT_HUM]</li> <li>PM 1 (µg/m3) Plantower PMS5003 [EXT_PM_1] or [EXT_PM_A_1], [EXT_PM_B_1] for each PM sensor in the case of the Living Lab Station</li> <li>PM 2.5 (µg/m3) Plantower PMS5003 [EXT_PM_25] or [EXT_PM_A_25], [EXT_PM_B_25] for each PM sensor in the case of the Living Lab Station</li> <li>PM 10 (µg/m3) Plantower PMS5003 [EXT_PM_10] or [EXT_PM_A_10], [EXT_PM_B_10] for each PM sensor in the case of the Living Lab Station</li> <li>PN between 0.3um<0.5um particle size (#/l) Plantower PMS5003 [EXT_PN_03] or [EXT_PN_A_03], [EXT_PN_B_03] for each PM sensor in the case of the Living Lab Station</li> <li>PN between 0.5um<1um particle size (#/l) Plantower PMS5003 [EXT_PN_05] or [EXT_PN_A_05], [EXT_PN_B_05] for each PM sensor in the case of the Living Lab Station</li> <li>PN between 1m<2.5um particle size (#/l) Plantower PMS5003 [EXT_PN_1] or [EXT_PN_A_1], [EXT_PN_B_1] for each PM sensor in the case of the Living Lab Station</li> <li>PN between 2.5m<5um particle size (#/l) Plantower PMS5003 [EXT_PN_25] or [EXT_PN_A_25], [EXT_PN_B_25] for each PM sensor in the case of the Living Lab Station</li> <li>PN between 5m<10um particle size (#/l) Plantower PMS5003 [EXT_PN_5] or [EXT_PN_A_5], [EXT_PN_B_5] for each PM sensor in the case of the Living Lab Station</li> <li>PN between >10um particle size (#/l) Plantower PMS5003 [EXT_PN_10] or [EXT_PN_A_10], [EXT_PN_B_10] for each PM sensor in the case of the Living Lab Station</li> </ul> <p>The files with the _processed suffix, are processed files which:</p> <p>1. Resample the data using pandas resampling with mean() -<a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.resample.html"> reference here</a></p> <p>2. Clean NaN and wrong readings.</p> <p>3. Add calculations for electrochemical sensors based on <a href="https://docs.smartcitizen.me/Components/Gas%20Pro%20Sensor%20Board/Electrochemical%20Sensors/#sensor-calibration">this methodology</a></p> <p><strong>How to find the data</strong></p> <p>Each yaml file contains the description of a test. Each test is comprised of recordings of several devices in the same location and during the same period. Each yaml file is comprised of the following fields:</p> <ul> <li>author: who has been in charge of performing the test (internal reference - not relevant)</li> <li>comment: describing in general terms what was done in the test, and with what purpose</li> <li>commit: the firmware commit (in the case of Smart Citizen devices) with which the test was performed, for development purposes only</li> <li>devices: a descriptor containing different fields for traceability (below)</li> <li>id: the test name</li> <li>project: within the test was performed, in this case it is always iscape</li> <li>report: if there is any report analysing the test</li> <li>type_test: indoor, oudoor test or other.</li> </ul> <p><strong>Description of devices entry</strong></p> <p>For each device that was used in the test, two generic types are used:</p> <ul> <li>low cost sensors (type: STATION or KIT)</li> <li>high end sensors (type: REFERENCE)</li> </ul> <p>For <strong>low cost Smart Citizen sensors</strong>, the fields are:</p> <ul> <li>alphasense: electrochemical sensors device ids, by pollutant (for manufacturer calibration) and slots in which they were placed</li> <li>device_id: device id in Smartcitizen API</li> <li>fileNameInfo: not used</li> <li>fileNameProc: (only if source = csv is specified) 2019-03_EXT_UCD_URBAN_BACKGROUND_API_CITY_COUNCIL_REF.csv</li> <li>fileNameRaw: (only if source = csv is used) raw file name</li> <li>frequency: original recording frequency</li> <li>location: for timezone correction only, not accurate</li> <li>max_date: last recording date</li> <li>min_date: first recording date</li> <li>name: self-explanatory</li> <li>pm_sensor: if there was a pm sensor connected (all of them are PMS5003 if no sensor is specified)</li> <li>source: api or csv</li> <li>type: STATION (KIT + Alphasense + PM board with two PMS5003) or KIT</li> <li>version: smartcitizen hardware version</li> </ul> <p>For <strong>high end</strong> sensors, the fields are:</p> <ul> <li>channels: which channels the device was recording for internal convertion <ul> <li>names: which are the columns in the csv file</li> <li>pollutants: which pollutants do they respectively refer to</li> <li>units: the units of these pollutants</li> </ul> </li> <li>equipment: the brand of the analyser</li> <li>fileNameProc: same as above</li> <li>fileNameRaw: same as above</li> <li>index: format in which the timeindex is done, for parsing purposes <ul> <li>format: (example '%Y-%m-%d %H:%M:%S')</li> <li>frequency: frequency at which the device was recorded</li> <li>name: column name</li> </ul> </li> <li>location: same as above</li> <li>name: name of the device</li> <li>type: REFERENCE (always for these devices)</li> <li>source: csv</li> </ul> <p><strong>iSCAPE Dataset Reference Numbers:</strong></p> <p>The datasets here presented are related to the following iSCAPE dataset reference numbers:</p> <ul> <li>DS_TS_049</li> <li>DS_TS_050</li> <li>DS_TS_051</li> <li>DS_TS_052</li> <li>DS_TS_053</li> <li>DS_TS_055</li> <li>DS_TS_056</li> <li>DS_TS_057</li> <li>DS_TS_058</li> </ul>
Raw and analyzed data for manuscript: "An open-source surface barrier discharge plasma pretreatment for reduced cracking of outdoor wood coatings"
<p><strong>Highlights:</strong></p> <ul> <li>Surface barrier discharges are an affordable and available plasma technology for industrial, laboratory and home-workshop applications.</li> <li>Plasma pretreatments had no impact on the appearance of different protective wood coating for outdoor usage.</li> <li>The weathering performance of outdoor wood coatings improved by plasma, showing less cracks and less biotic factors.</li> </ul>
A Dataset of Outdoor RSS Measurements for Localization
<p><strong>Update: </strong>New version includes additional samples taken in November 2022.</p> <p><strong>Dataset Description</strong></p> <p>This dataset is a large-scale set of measurements for RSS-based localization. The data consists of received signal strength (RSS) measurements taken using the POWDER Testbed at the University of Utah. Samples include either 0, 1, or 2 active transmitters.</p> <p>The dataset consists of 5,214 unique samples, with transmitters in 5,514 unique locations. The majority of the samples contain only 1 transmitter, but there are small sets of samples with 0 or 2 active transmitters, as shown below. Each sample has RSS values from between 10 and 25 receivers. The majority of the receivers are stationary endpoints fixed on the side of buildings, on rooftop towers, or on free-standing poles. A small set of receivers are located on shuttles which travel specific routes throughout campus.</p> <table> <tbody> <tr> <th>Dataset Description</th> <th>Sample Count</th> <th>Receiver Count</th> </tr> </tbody> <tbody> <tr> <td>No-Tx Samples</td> <td>46</td> <td>10 to 25</td> </tr> <tr> <td>1-Tx Samples</td> <td>4822</td> <td>10 to 25</td> </tr> <tr> <td>2-Tx Samples</td> <td>346</td> <td>11 to 12</td> </tr> </tbody> </table> <p>The transmitters for this dataset are handheld walkie-talkies (Baofeng BF-F8HP) transmitting in the FRS/GMRS band at 462.7 MHz. These devices have a rated transmission power of 1 W. The raw IQ samples were processed through a 6 kHz bandpass filter to remove neighboring transmissions, and the RSS value was calculated as follows:</p> <p>\(RSS = \frac{10}{N} \log_{10}\left(\sum_i^N x_i^2 \right) \)</p> <table> <tbody> <tr> <th>Measurement Parameters</th> <th>Description</th> </tr> </tbody> <tbody> <tr> <td>Frequency</td> <td>462.7 MHz</td> </tr> <tr> <td>Radio Gain</td> <td>35 dB</td> </tr> <tr> <td>Receiver Sample Rate</td> <td>2 MHz</td> </tr> <tr> <td>Sample Length</td> <td>N=10,000</td> </tr> <tr> <td>Band-pass Filter</td> <td>6 kHz</td> </tr> <tr> <td>Transmitters</td> <td>0 to 2</td> </tr> <tr> <td>Transmission Power</td> <td>1 W</td> </tr> </tbody> </table> <p>Receivers consist of Ettus USRP X310 and B210 radios, and a mix of wide- and narrow-band antennas, as shown in the table below Each receiver took measurements with a receiver gain of 35 dB. However, devices have different maxmimum gain settings, and no calibration data was available, so all RSS values in the dataset are uncalibrated, and are only relative to the device.</p> <p><strong>Usage Instructions</strong></p> <p>Data is provided in <code>.json</code> format, both as one file and as split files.</p> <pre><code>import json data_file = 'powder_462.7_rss_data.json' with open(data_file) as f: data = json.load(f) </code></pre> <p>The <code>json</code> data is a dictionary with the sample timestamp as a key. Within each sample are the following keys:</p> <ul> <li><code>rx_data</code>: A list of data from each receiver. Each entry contains RSS value, latitude, longitude, and device name.</li> <li><code>tx_coords</code>: A list of coordinates for each transmitter. Each entry contains latitude and longitude.</li> <li><code>metadata</code>: A list of dictionaries containing metadata for each transmitter, in the same order as the rows in <code>tx_coords</code></li> </ul> <p><strong>File Separations and Train/Test Splits</strong></p> <p>In the <code>separated_data.zip</code> folder there are several train/test separations of the data.</p> <ul> <li><code>all_data</code> contains all the data in the main JSON file, separated by the number of transmitters.</li> <li><code>stationary</code> consists of 3 cases where a stationary receiver remained in one location for several minutes. This may be useful for evaluating localization using mobile shuttles, or measuring the variation in the channel characteristics for stationary receivers.</li> <li><code>train_test_splits</code> contains unique data splits used for training and evaluating ML models. These splits only used data from the single-tx case. In other words, the union of each splits, along with <code>unused.json</code>, is equivalent to the file <code>all_data/single_tx.json</code>. <ul> <li>The <code>random</code> split is a random 80/20 split of the data.</li> <li><code>special_test_cases</code> contains the stationary transmitter data, indoor transmitter data (with high noise in GPS location), and transmitters off campus.</li> <li>The <code>grid</code> split divides the campus region in to a 10 by 10 grid. Each grid square is assigned to the training or test set, with 80 squares in the training set and the remainder in the test set. If a square is assigned to the test set, none of its four neighbors are included in the test set. Transmitters occuring in each grid square are assigned to train or test. One such random assignment of grid squares makes up the <code>grid</code> split.</li> <li>The <code>seasonal</code> split contains data separated by the month of collection, in April, July, or November</li> <li>The <code>transportation</code> split contains data separated by the method of movement for the transmitter: walking, cycling, or driving. The <code>non-driving.json</code> file contains the union of the walking and cycling data.</li> <li><code>campus.json</code> contains the on-campus data, so is equivalent to the union of each split, not including <code>unused.json</code>.</li> </ul> </li> </ul> <p><strong>Digital Surface Model</strong></p> <p>The dataset includes a digital surface model (DSM) from a State of Utah 2013-2014 LiDAR <a href="https://doi.org/10.5069/G9TH8JNQ">survey</a>. This map includes the University of Utah campus and surrounding area. The DSM includes buildings and trees, unlike some digital elevation models.</p> <p>To read the data in python:</p> <pre><code>import rasterio as rio import numpy as np import utm dsm_object = rio.open('dsm.tif') dsm_map = dsm_object.read(1) # a np.array containing elevation values dsm_resolution = dsm_object.res # a tuple containing x,y resolution (0.5 meters) dsm_transform = dsm_object.transform # an Affine transform for conversion to UTM-12 coordinates utm_transform = np.array(dsm_transform).reshape((3,3))[:2] utm_top_left = utm_transform @ np.array([0,0,1]) utm_bottom_right = utm_transform @ np.array([dsm_object.shape[0], dsm_object.shape[1], 1]) latlon_top_left = utm.to_latlon(utm_top_left[0], utm_top_left[1], 12, 'T') latlon_bottom_right = utm.to_latlon(utm_bottom_right[0], utm_bottom_right[1], 12, 'T') </code></pre> <p><strong>Dataset Acknowledgement:</strong> This DSM file is acquired by the State of Utah and its partners, and is in the public domain and can be freely distributed with proper credit to the State of Utah and its partners. The State of Utah and its partners makes no warranty, expressed or implied, regarding its suitability for a particular use and shall not be liable under any circumstances for any direct, indirect, special, incidental, or consequential damages with respect to users of this product.</p> <p><strong>DSM DOI:</strong> <a href="https://doi.org/10.5069/G9TH8JNQ">https://doi.org/10.5069/G9TH8JNQ</a></p>
Dataset from: 'Tropical deforestation accelerates local warming and loss of safe outdoor working hours'
<p>Abstract:</p> <p>'Climate change has increased heat exposure in many parts of the tropics, negatively impacting outdoor worker productivity and health. Although it is known that tropical deforestation causes local warming, the extent to which this warming affects people across the tropics is unknown. Here, we combine worker health guidelines with satellite, reanalysis, and population data to investigate how increases in local temperatures associated with recent deforestation (2003-2018) affects outdoor working conditions across low-latitude countries, and how future global climate change will magnify heat exposure for people in deforested areas. We find that the local warming associated with just 15 years of deforestation has caused losses in safe thermal working conditions for 2.8 million outdoor workers. We also show recent large-scale forest loss caused particularly large impacts on populations in locations such as the Brazilian states of Mato Grosso and Pará. Future global warming and additional forest loss will magnify these impacts.'</p>
RookID: an annotated dataset of vocalisations produced by individually-identified rooks housed together in an outdoors aviary in France
<p>A dataset of annotated recordings of a captive colony of rooks, recorded in Strasbourg, France in 2020 and 2021. Each rook was individually identifiable with leg rings. All recordings were taken in the morning a few hours after sunrise, when the birds were most vocally active. The colony was housed outdoors, so other noises are present, including both biotic (most notably various birds, human voices, and other animals) and abiotic (mostly car and train noises).</p> <p>Audio files (.wav): recorded at 48 kHz, 16-bit using 1 to 3 Song Meter 4 recorders (Wildlife Acoustics). Each recorder had two microphone with different gains to maximise dynamic range. The files were then manually synchronised and merged into multichannel (2 to 6) files.</p> <p>Label files (.tsv): Labels corresponding to each recording (each pair has the same name), noting the time stamps and individual emitter for each vocalisation. A single observer annotated all the recordings. Only rook vocalisations from the captive colony were annotated, not other bird vocalisations or the various noises in the data. The annotations consist of tables with 5 columns: </p> <ul> <li>Source: the individual producing the vocalisation. Note that only the bird's name is indicated. "Inc" and "Pls" are special cases: the first was for when identity could not be determined, the second when multiple individuals vocalised at once in such a manner that individuals could not be separated</li> <li>Start: starting time point for the vocalisation, in seconds (determined as the earliest point when the vocalisation was heard on any channel)</li> <li>End: ending time point for the vocalisation, in seconds (determined as the last point when the vocalisation was head on any channel)</li> <li>Event: gives information for the bird's activity at the time of the vocalisation, but largely in abbreviated form. One particular case is "sing", which correspond to vocalisations part of a song bout (which are defined as sequences of different vocalisations separated by less than approximately 10 seconds).</li> <li>Comment: other observations regarding the vocalisation. These are usually not standardised compared to the Event column. One special case is for "Pls": the Comment column then bears information regarding the identity of the individuals involved.</li> </ul> <p> </p> <p>This dataset was used in our article "Acoustic detection and identification of individual rooks in field recordings using multi-task neural networks", to train neural networks to identify individual rooks. The dataset was therefore randomly split into train-validation-test datasets. For reproducibility, we provide the "splitting.csv" which contains the information pertaining to which files go in each dataset, and two scripts to do the split automatically.</p> <p>To do so: download and unpack the RookID folder somewhere on your computer, then download splitting.csv and either of the scripts to the same location. Both scripts will MOVE, not copy, the files to new folders corresponding to each dataset.</p> <ul> <li>with split_data.R: open the scrip in an RStudio environment, edit the out_path variable to the desired location, and run the script</li> <li>with split_data.py: run the following command line: python /path/to/split_data.py --out_path path/to/desired/location (note that the script will automatically create the necessary tree structure)</li> <li>Both scripts can be run without editing the out_path variables, in which case the new folders will be created at the same location</li> </ul> <p> </p> <p>For further information, see our code at <a href="https://gitlab.com/kimartin/rook-vocalisation-detection">https://gitlab.com/kimartin/rook-vocalisation-detection</a></p> <p>For any inquiries, please contact Killian Martin (<a href="mailto:killian.martin@ens-lyon.fr?subject=Inquiry%20about%20the%20RookID%20dataset">killian.martin@ens-lyon.fr</a>)</p>
Replication data for Global variation in the preferred temperature for recreational outdoor activity
<p><strong>Description</strong></p> <p>This dataset contains the processed data used for the statistical analysis in Linsenmeier, M. (2024): <a href="https://doi.org/10.1016/j.jeem.2024.103032">Global variation in the preferred temperature for recreational outdoor activity</a>, published in the Journal of Environmental Economics and Management.</p> <p>The main data on temperature and rainfall are from ERA5 reanalysis (Hersbach et al. 2018). Data on mobile phone activity are from the Google Mobility Reports. Data on GDP per capita are from the World Bank.</p> <p><strong>Acknowledgements</strong></p> <p>The data contain modified Copernicus Climate Change Service information 2020. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.</p> <p><strong>Bibliography</strong></p> <ul> <li>Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2018): ERA5 hourly data on single levels from 1959 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). 10.24381/cds.adbb2d47</li> </ul>
A Reproducible Analysis of RSSI Fingerprinting for Outdoors Localization Using Sigfox: Preprocessing and Hyperparameter Tuning (datasets)
<p>The train/validation/test sets used in the study "<strong>A Reproducible Analysis of RSSI Fingerprinting for Outdoors Localization Using Sigfox: Preprocessing and Hyperparameter Tuning</strong>".</p> <p>Preprint:<a href="https://arxiv.org/abs/1908.06851"> https://arxiv.org/abs/1908.06851</a></p> <p>Published paper: <a href="https://ieeexplore.ieee.org/document/8911792">https://ieeexplore.ieee.org/document/8911792</a></p> <p> </p> <p>The dataset used to create these sets was published in:</p> <p><a href="http://www.mdpi.com/2306-5729/3/2/13">http://www.mdpi.com/2306-5729/3/2/13</a></p> <p>The full dataset is available here:</p> <pre><a href="https://doi.org/10.5281/zenodo.1212478">https://doi.org/10.5281/zenodo.1212478</a> </pre> <p>The credit for the creation of the dataset goes to Aernouts, Michiel; Berkvens, Rafael; Van Vlaenderen, Koen; and Weyn, Maarten.</p> <p> </p>
Hyperspectral environmental illumination maps for outdoor and indoor scenes
<p>This repository contains a dataset of hyperspectral illumination maps collected from 6 outdoor and 4 indoor scenes.</p> <p> </p> <p>If you use this dataset in your research, please cite:</p> <p> </p> <p>Takuma Morimoto, João M. M. Linhares, Sérgio M. C. Nascimento, and Hannah E. Smithson, “How many surfaces can you distinguish by color? Real environmental lighting increases discriminability of surface colors,” Optics Express (in press).</p> <p> </p> <p>Technical details about data acquisition are described in:</p> <p> </p> <p>Takuma Morimoto, Sho Kishigami, João M.M. Linhares, Sérgio M.C. Nascimento, and Hannah E. Smithson, “Hyperspectral environmental illumination maps: characterizing directional spectral variation in natural environments,” Optics Express, 27, 22, 32277 - 32293. (2019). <a href="https://doi.org/10.1364/OE.27.032277">https://doi.org/10.1364/OE.27.032277</a></p> <p> </p> <p>Each file includes the following formats:</p> <p> </p> <ol> <li><strong>png</strong>: RGB image for visualization.<br><br></li> <li><strong>mat</strong>: Hyperspectral image with wavelengths from 400 nm to 700 nm in 10 nm steps. Each pixel value represents spectral radiance in W m−2 sr−1 nm−1. The image and wavelength range are stored in the variables ‘radiance’ and ‘wls’, respectively.</li> </ol> <p>The images have an average spatial resolution of 1019 (height) × 2035 (width) across 10 scenes.</p> <p>File names follow the format X_sceneY, where X is the scene type (outdoor or indoor) and Y is the scene number.</p> <p> </p> <p>For Python users, the mat file can be loaded using e.g. scipy.io.loadmat. More information: <a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.loadmat.html">https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.loadmat.html</a></p> <p> </p> <p>Note: To use for hyperspectral renderings (e.g., Mitsuba), convert the hyperspectral image to the OpenEXR format.</p>
Dataset: Environmental Impact on the Long-Term Connectivity and Link Quality of an Outdoor LoRa Network
<p>This repository contains the long-term connectivity and link quality dataset collected on <a href="https://chirpbox.github.io/">ChirpBox</a> over 4 months (May -- September 2021) in the city of Shanghai, China. </p> <p>In addition to the dataset itself, we provide evaluation scripts for data analysis and visualization, in order to facilitate data exploration and re-use. To make it clear how to use the scripts, we provide a <em>Jupyter notebook -- </em> <strong>dataset.ipynb</strong> for dataset visualization.</p> <p><strong>List of files:</strong></p> <ol> <li><em>dataset_03052021_15092021.csv</em> <ul> <li>The dataset includes LoRa connectivity and link quality, as well as environmental information, collected from May 3 to September 15, 2021.</li> </ul> </li> <li><em>data_analysis.py</em> <ul> <li>The script for dataset analysis and visualization. One can use the functions in this script to derive network-level statistics (e.g., in terms of average number of correctly-exchanged packets), link-level statistics (e.g., in terms of SNR, RSS, and PRR), and node-level statistics(e.g., in terms of number of neighbours and temperature evolution over time).</li> </ul> </li> <li><em>metadata_processing.py</em> <ul> <li>The script for pre-processing metadata into CSV files. One can use the functions in this script to convert metadata for each measurement saved in TXT and JSON formats to CSV files that include attributes such as link quality, connectivity, and environmental information, an example of which is <strong>dataset_03052021_15092021.csv</strong>.</li> </ul> </li> <li><em>dataset.ipynb </em> <ul> <li>The Jupiter notebook contains examples of visualization and metadata pre-processing of datasets with functions in <strong>data_analysis.py</strong> and <strong>metadata_processing.py</strong>.</li> </ul> </li> <li><em>topology_map.png</em> <ul> <li>The node deployment map used to create topology figures. A usage example is <strong>Figure 1</strong> shown in the notebook <strong>dataset.ipynb</strong>.</li> </ul> </li> <li><em>dataset_metadata.zip</em> <ul> <li>The dataset metadata is stored in TXT and JSON formats. Among them, link quality, connectivity and on-board sensor data are stored in TXT files and weather information are stored in JOSN files.</li> </ul> </li> <li><em>README.md</em> <ul> <li>The README.md explains all the files in this repository and gives some examples of how to use the provided scripts to analyze the dataset.</li> </ul> </li> </ol>
EOAD (Egocentric Outdoor Activity Dataset)
<p>EOAD is a collection of videos captured by wearable cameras, mostly of sports activities. It contains both visual and audio modalities.</p> <p>It was initiated by the <a href="https://www.vision.huji.ac.il/egoseg/videos/dataset.html">HUJI</a> and <a href="https://github.com/azuxmioy/fpvsum">FPVSum</a> egocentric activity datasets. However, the number of samples and diversity of activities for HUJI and FPVSum were insufficient. Therefore, we combined these datasets and populated them with new YouTube videos.</p> <p>The selection of videos was based on the following criteria:</p> <ul> <li>The videos should not include text overlays.</li> <li>The videos should contain natural sound (no external music)</li> <li>The actions in videos should be continuous (no cutting the scene or jumping in time)</li> </ul> <p>Video samples were trimmed depending on scene changes for long videos (such as <em>driving</em>, <em>scuba diving</em>, and <em>cycling</em>). As a result, a video may have several clips depicting egocentric actions. Hence, video clips were extracted from carefully defined time intervals within videos. The final dataset includes video clips with a single action and natural audio information.</p> <p>Statistics for EOAD:</p> <ul> <li><strong>30</strong> activities</li> <li><strong>303</strong> distinct videos</li> <li><strong>1392</strong> video clips</li> <li><strong>2243</strong> minutes labeled videos clips</li> </ul> <p>The detailed statistics for the selected datasets and the crawled videos clips from YouTube are given below:</p> <ul> <li><strong><a href="https://www.vision.huji.ac.il/egoseg/videos/dataset.html">HUJI</a></strong>: 49 distinct videos - 148 video clips for 9 activities (<em>driving</em>, <em>biking</em>, <em>motorcycle</em>, <em>walking</em>, <em>boxing</em>, <em>horse riding</em>, <em>running</em>, <em>skiing</em>, <em>stair climbing</em>)</li> <li><strong><a href="https://github.com/azuxmioy/fpvsum">FPVSum</a></strong>: 39 distinct videos - 124 video segments for 8 activities (<em>biking</em>, <em>horse riding</em>, <em>skiing</em>, <em>longboarding</em>, <em>rock climbing</em>, <em>scuba</em>, <em>skateboarding</em>, <em>surfing</em>)</li> <li><strong>YouTube</strong>: 216 distinct videos - 1120 video clips for 27 activities (<em>american football</em>, <em>basketball</em>, <em>bungee jumping</em>, <em>driving</em>, <em>go-kart</em>, <em>horse riding</em>, <em>ice hockey</em>, <em>jet ski</em>, <em>kayaking</em>, <em>kitesurfing</em>, <em>longboarding</em>, <em>motorcycle</em>, <em>paintball</em>, <em>paragliding</em>, <em>rafting</em>, <em>rock climbing</em>, <em>rowing</em>, <em>running</em>, <em>sailing</em>, <em>scuba diving</em>, <em>skateboarding</em>, <em>soccer</em>, <em>stair climbing</em>, <em>surfing</em>, <em>tennis</em>, <em>volleyball</em>, <em>walking</em>)</li> </ul> <p>The video clips used for training, validation and test sets for each activity are listed in <em>Table 1</em>. Multiple video clips may belong to a single video because of trimming it for some reasons (i.e., scene cut, temporary overlayed text on videos, or video parts unrelated to activities).</p> <p>While splitting the dataset, the minimum number of videos for each activity was selected as 8. Additionally, the video samples were divided as 50%, 25%, and 25% for training (minimum four videos), validation (minimum two videos), and testing (minimum two videos), respectively. On the other hand, videos were split according to the raw video footage to prevent the mixing of similar video clips (having the same actors and scenes) into training, validation, and test sets. Therefore, we ensured that the video clips trimmed from the same videos were split together into training, validation, or test sets to satisfy a fair comparison.</p> <p>Some activities have continuity throughout the video, such as <em>scuba</em>, <em>longboarding</em>, or <em>riding horse</em>, which also have an equal number of video segments with the number of videos. However, some activities, such as skating, occurred in a short time, making the number of video segments higher than the others. As a result, the number of video clips for training, validation, and test sets was highly imbalanced for the selected activities (i.e., <em>jet ski</em> and <em>rafting</em> have 4; however, <em>soccer</em> has 99 video clips for training).</p> <p><strong> Table 1 - Dataset splitting for EOAD</strong></p> <table align="center"> <thead> <tr> <th> </th> <th> </th> <th> <p><strong>Train</strong></p> </th> <th> </th> <th> <p><strong>Validation</strong></p> </th> <th> </th> <th> <p><strong>Test</strong></p> </th> <th> </th> </tr> </thead> <tbody> <tr> <td> </td> <td> <p><strong>Action Label</strong></p> </td> <td> <p><strong>#Clips</strong></p> </td> <td> <p><strong>Total Duration</strong></p> </td> <td> <p><strong>#Clips</strong></p> </td> <td> <p><strong>Total Duration</strong></p> </td> <td> <p><strong>#Clips</strong></p> </td> <td> <p><strong>Total Duration</strong></p> </td> </tr> <tr> <td> </td> <td> <p><strong>AmericanFootball</strong></p> </td> <td> <p>34</p> </td> <td> <p>00:06:09</p> </td> <td> <p>36</p> </td> <td> <p>00:05:03</p> </td> <td> <p>9</p> </td> <td> <p>00:01:20</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Basketball</strong></p> </td> <td> <p>43</p> </td> <td> <p>01:13:22</p> </td> <td> <p>19</p> </td> <td> <p>00:08:13</p> </td> <td> <p>10</p> </td> <td> <p>00:28:46</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Biking</strong></p> </td> <td> <p>9</p> </td> <td> <p>01:58:01</p> </td> <td> <p>6</p> </td> <td> <p>00:32:22</p> </td> <td> <p>11</p> </td> <td> <p>00:36:16</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Boxing</strong></p> </td> <td> <p>7</p> </td> <td> <p>00:24:54</p> </td> <td> <p>11</p> </td> <td> <p>00:14:14</p> </td> <td> <p>5</p> </td> <td> <p>00:17:30</p> </td> </tr> <tr> <td> </td> <td> <p><strong>BungeeJumping</strong></p> </td> <td> <p>7</p> </td> <td> <p>00:02:22</p> </td> <td> <p>4</p> </td> <td> <p>00:01:36</p> </td> <td> <p>4</p> </td> <td> <p>00:01:31</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Driving</strong></p> </td> <td> <p>19</p> </td> <td> <p>00:37:23</p> </td> <td> <p>9</p> </td> <td> <p>00:24:46</p> </td> <td> <p>9</p> </td> <td> <p>00:29:23</p> </td> </tr> <tr> <td> </td> <td> <p><strong>GoKart</strong></p> </td> <td> <p>5</p> </td> <td> <p>00:40:00</p> </td> <td> <p>3</p> </td> <td> <p>00:11:46</p> </td> <td> <p>3</p> </td> <td> <p>00:19:46</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Horseback</strong></p> </td> <td> <p>5</p> </td> <td> <p>01:15:14</p> </td> <td> <p>5</p> </td> <td> <p>01:02:26</p> </td> <td> <p>2</p> </td> <td> <p>00:20:38</p> </td> </tr> <tr> <td> </td> <td> <p><strong>IceHockey</strong></p> </td> <td> <p>52</p> </td> <td> <p>00:19:22</p> </td> <td> <p>46</p> </td> <td> <p>00:20:34</p> </td> <td> <p>10</p> </td> <td> <p>00:36:59</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Jetski</strong></p> </td> <td> <p>4</p> </td> <td> <p>00:23:35</p> </td> <td> <p>5</p> </td> <td> <p>00:18:42</p> </td> <td> <p>6</p> </td> <td> <p>00:02:43</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Kayaking</strong></p> </td> <td> <p>28</p> </td> <td> <p>00:43:11</p> </td> <td> <p>22</p> </td> <td> <p>00:14:23</p> </td> <td> <p>4</p> </td> <td> <p>00:11:05</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Kitesurfing</strong></p> </td> <td> <p>30</p> </td> <td> <p>00:21:51</p> </td> <td> <p>17</p> </td> <td> <p>00:05:38</p> </td> <td> <p>6</p> </td> <td> <p>00:01:32</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Longboarding</strong></p> </td> <td> <p>5</p> </td> <td> <p>00:15:40</p> </td> <td> <p>4</p> </td> <td> <p>00:18:03</p> </td> <td> <p>4</p> </td> <td> <p>00:09:11</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Motorcycle</strong></p> </td> <td> <p>20</p> </td> <td> <p>00:49:38</p> </td> <td> <p>21</p> </td> <td> <p>00:13:53</p> </td> <td> <p>8</p> </td> <td> <p>00:20:30</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Paintball</strong></p> </td> <td> <p>7</p> </td> <td> <p>00:33:52</p> </td> <td> <p>4</p> </td> <td> <p>00:12:08</p> </td> <td> <p>4</p> </td> <td> <p>00:08:52</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Paragliding</strong></p> </td> <td> <p>11</p> </td> <td> <p>00:28:42</p> </td> <td> <p>4</p> </td> <td> <p>00:10:16</p> </td> <td> <p>4</p> </td> <td> <p>00:19:50</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Rafting</strong></p> </td> <td> <p>4</p> </td> <td> <p>00:15:41</p> </td> <td> <p>3</p> </td> <td> <p>00:07:27</p> </td> <td> <p>3</p> </td> <td> <p>00:06:13</p> </td> </tr> <tr> <td> </td> <td> <p><strong>RockClimbing</strong></p> </td> <td> <p>6</p> </td> <td> <p>00:49:38</p> </td> <td> <p>2</p> </td> <td> <p>00:21:59</p> </td> <td> <p>2</p> </td> <td> <p>00:18:50</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Rowing</strong></p> </td> <td> <p>5</p> </td> <td> <p>00:47:05</p> </td> <td> <p>3</p> </td> <td> <p>00:13:21</p> </td> <td> <p>3</p> </td> <td> <p>00:03:26</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Running</strong></p> </td> <td> <p>21</p> </td> <td> <p>01:21:56</p> </td> <td> <p>19</p> </td> <td> <p>00:46:29</p> </td> <td> <p>11</p> </td> <td> <p>00:42:59</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Sailing</strong></p> </td> <td> <p>7</p> </td> <td> <p>00:39:30</p> </td> <td> <p>4</p> </td> <td> <p>00:14:39</p> </td> <td> <p>6</p> </td> <td> <p>00:15:43</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Scuba</strong></p> </td> <td> <p>5</p> </td> <td> <p>00:35:02</p> </td> <td> <p>3</p> </td> <td> <p>00:23:43</p> </td> <td> <p>2</p> </td> <td> <p>00:18:52</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Skate</strong></p> </td> <td> <p>91</p> </td> <td> <p>00:15:53</p> </td> <td> <p>30</p> </td> <td> <p>00:07:01</p> </td> <td> <p>10</p> </td> <td> <p>00:02:03</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Ski</strong></p> </td> <td> <p>14</p> </td> <td> <p>01:48:15</p> </td> <td> <p>17</p> </td> <td> <p>01:01:59</p> </td> <td> <p>7</p> </td> <td> <p>00:39:15</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Soccer</strong></p> </td> <td> <p>102</p> </td> <td> <p>00:48:39</p> </td> <td> <p>52</p> </td> <td> <p>00:13:17</p> </td> <td> <p>16</p> </td> <td> <p>00:06:54</p> </td> </tr> <tr> <td> </td> <td> <p><strong>StairClimbing</strong></p> </td> <td> <p>6</p> </td> <td> <p>01:05:32</p> </td> <td> <p>6</p> </td> <td> <p>00:17:18</p> </td> <td> <p>5</p> </td> <td> <p>00:20:22</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Surfing</strong></p> </td> <td> <p>23</p> </td> <td> <p>00:12:51</p> </td> <td> <p>17</p> </td> <td> <p>00:06:52</p> </td> <td> <p>10</p> </td> <td> <p>00:07:04</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Tennis</strong></p> </td> <td> <p>34</p> </td> <td> <p>00:27:04</p> </td> <td> <p>9</p> </td> <td> <p>00:06:03</p> </td> <td> <p>9</p> </td> <td> <p>00:03:14</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Volleyball</strong></p> </td> <td> <p>87</p> </td> <td> <p>00:19:14</p> </td> <td> <p>35</p> </td> <td> <p>00:07:46</p> </td> <td> <p>7</p> </td> <td> <p>00:18:58</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Walking</strong></p> </td> <td> <p>49</p> </td> <td> <p>00:43:02</p> </td> <td> <p>36</p> </td> <td> <p>00:38:25</p> </td> <td> <p>10</p> </td> <td> <p>00:10:23</p> </td> </tr> <tr> <td> <p><strong>Total</strong></p> </td> <td> <p>30</p> </td> <td> <p>740</p> </td> <td> <p>20:22:37</p> </td> <td> <p>452</p> </td> <td> <p>09:20:23</p> </td> <td> <p>200</p> </td> <td> <p>08:00:08</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>EOAD Code Repository</strong></p> <p>Scripts for downloading raw videos and trim them in to video clips are provided in <a href="https://github.com/maliarabaci/eoad">this GitHub</a> repository.</p> <p>Regarding the questions, please contact <strong><em>mali.arabaci@gmail.com.</em></strong></p>
MCR LTER: Coral Reef: Community structure outdoor flume data in support of Edmunds 2019 Marine Biology
This dataset contains data in support of Edmunds, P.J., S.S. Doo, R.C. Carpenter, 'Changes in coral reef community structure in response to year-long incubations under contrasting pCO2 regimes', Marine Biology, 2019, doi:10.1007/s00227-019-3540-2. Here, the effects of ocean acidification (OA) on back reef communities from Mo'orea, French Polynesia (17.492S, 149.826W), were tested from 12 November 2015 to 16 November 2016 in outdoor flumes maintained at various mean pCO2 levels. Change in mass and percent cover were recorded monthly. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2018). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.
Energy efficiency on Philips Lightings products for outdoor lighting
<p>The dataset is a compilation of specifications and performance metrics for different lighting products from Philips Lighting catalogs. It spans various products across different technology types and years, which suggests a focus on the evolution and comparison of lighting efficiency over time.</p> <p>Here are some key points about the dataset:</p> <p>- **Product Information**: Each entry in the `Nombre` column provides specific details about a Philips Lighting product, likely including the model and technical specifications.</p> <p>- **Technology Classification**: The `Tecno` column classifies each product according to its lighting technology, such as LED, CDM, SOX, etc. This allows for analysis across different types of lighting technologies.</p> <p>- **Energy Consumption and Efficiency**: The dataset includes data on energy consumption (`Consumo`) and efficiency (`Efi(lm/w)` and `Efi2`). These metrics are crucial for understanding the energy cost of running the lights and for analyzing improvements in energy efficiency over time. Efi is the calculated energy efficiency from the catalogue data and the Efi2 is the reported energy eficiency.</p> <p>- **Light Output and Quality**: The `Lumens` and `CCT` columns provide information on the brightness and color temperature of the lighting products. This is valuable for assessing the quality and suitability of the light for various applications.</p> <p>- **Economic Considerations**: The `Precio` column, while not filled in for all entries, would give insights into the economic aspect of the lighting products, potentially allowing for cost-benefit analysis.</p> <p>- **Temporal Trends**: The `Año` column indicates the year associated with the product, which can be used to track changes and advancements in lighting technology over time.</p> <p>- **Product Longevity**: The `Vida` column, although unspecified in the dataset preview, would generally relate to the lifespan of the lighting product, an important factor in both consumer choice and sustainability considerations.</p> <p>In summary, this dataset serves as a resource for analyzing Philips Lighting products' performance over time, understanding trends in lighting technology efficiency, and potentially assisting in strategic decisions related to product development, marketing, and sustainability efforts.</p>
Outdoor air pollution impacts chronic obstructive pulmonary disease deaths in South Asia and China: a systematic review and meta-analysis
<p><strong>Background: </strong>Chronic obstructive pulmonary disease (COPD) is among leading causes of death globally. Exposure to outdoor pollution is an important cause for increased mortality and morbidity. This study presents a systemic review regarding the impact of outdoor pollution on COPD mortality in South Asia and China.</p> <p><strong>Methods: </strong>A systematic search was conducted from 1990 to June 30<sup>th</sup> 2020 in English electronic databases: PubMed, Google Scholar and CDSR (Cochrane Database of Systematic Reviews) following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The following terms were used: Chronic Obstructive Pulmonary disease OR COPD OR Chronic Bronchitis OR Emphysema OR COPD Deaths OR Chronic Obstructive Lung Disease OR Airflow Obstruction OR Chronic Airflow Obstruction OR Airflow Obstruction, Chronic OR Bronchitis, Chronic AND Mortality OR Death OR Deceased AND Outdoor pollution, ambient pollution was conducted.</p> <p><strong>Results:</strong> Out of 1899 papers screened only 17 were found eligible to be included. Subjects with COPD exposed to higher levels of outdoor air pollution had a 49% higher risk of death as compared to COPD subjects exposed to lower levels of outdoor air pollution. When taking common air pollutants individually into consideration, PM10 had an odds ratio (OR) of 1.99 respectively at CI 95%, whereas SO2 had OR of 1.8 at 95% CI, and NO2 had an OR of 1.23 OR at 95% CI. These values suggest that there is an effect of outdoor pollution on COPD but not to a significant level.</p> <p><strong>Conclusion: </strong>Despite heterogeneity across selected studies, individuals exposed to outdoor pollutants were found to be at risk of COPD mortality. Though it appears to have risk, COPD mortality was not significantly associated with outdoor pollutants. Controlling air pollution can substantially decrease the risk of COPD in South Asia and China. Further researches including more prospective and longitudinal studies are urgently needed in COPD sub-groups.</p>
ScienceDex guides
Understand access before you commit
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.