Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
412
datasets available to search
ShareScore release 0.7.1
Dataset results
412 results for “sensor data”
Soil moisture and climate data from SmartCityTrees sensors in Amersfoort
<p>Within the frame of the SCOREwater project, the City of Amersfoort commissioned <a href="https://hoefakker.com/boomspecialisten/smart-city-bodemvochtsensor/">Hoefakker</a> to install Teneo soil moisture and climate monitor sensors at several locations in the Schothorst neighbourhood and the Central Railway area. These sensors have been branded as "SmartCityTrees" by Hoefakker. The sensors measure soil moisture, temperature and humidity. Schothorst and the Central Railway area differ in groundwater levels. In Schothorst the groundwater levels are higher (average height: 0.7 meter to 1.0 meter below ground level) than in the Central Railway area (average height: lower than 1.6 meter below ground level). The lowest soil moisture sensors are placed on 1.2 meter below ground level. As a result, in the Schothorst area the sensors are located close to or in the groundwater during winter. In the Central Railway area they are located far above the groundwater level. This makes both areas interesting to include. What all soil moisture sensors have in common is that they are all located nearby trees and in public spaces. Locations differ in terms of: being in the sun or in the shade, being in a green setting (unpaved, such as parks) or being in a paved setting, and being near surface water or not. Because of the differences between the locations the sensors have been installed in, data from the sensors can be used to investigate questions such as:</p> <ul> <li>What is the influence of the type of surface on soil moisture levels?</li> <li>Does the nearby presence of surface water affect soil moisture?</li> <li>What is the influence of heat on soil moisture?</li> <li>What is the relation between groundwater levels fluctuance and soil moisture?</li> <li>Are adjustments on in public spaces (on street level) helpful to improve the soil situation for a more climate adaptive city?</li> </ul> <p>The soil moisture dataset contains soil moisture VWC (volumetric water content, the ratio of water volume to soil volume). This is a percentage represented as a number between 0 and 1. 0.73 for example is 73%.</p> <p>The climate dataset contains temperature in degrees Celsius and the relative humidity. Relative humidity is a percentage represented as a number between 0 and 1 as well.</p> <p>Time period: from 2021-01-01 to 2022-12-31.</p>
Water quality data from s::can sensors in the Barcelona sewer system
<p>Within the frame of the SCOREwater project, BCASA and s::can installed water quality sensors in the sewer system in three neighbourhoods with different socio-economic characteristics (Poblenou, Sant Gervasi and Carmel) in the City Of Barcelona. To prevent vandalism, the exact location of the sensors cannot be disclosed. The sensors are monitoring physico-chemical parameters in the sewer network. These data can be used for multiple purposes, both related to predictive maintenance of the sewer network and life style analysis of inhabitants of Barcelona:</p> <ul> <li>a better operation and maintenance of the sewer network</li> <li>minimizing odor episodes and corrosion from H2S</li> <li>try to prevent blocking from sediments</li> <li>detect spills into the sewer system from a construction site or illegal discharge</li> <li>learn population habits from analyzing the waste water from the three different neighborhoods</li> <li>to learn about how the inhabitants use pharmaceuticals or other compounds (perhaps abusing of it).</li> </ul>
HRV-ACC: a dataset with R-R intervals and accelerometer data for the diagnosis of psychotic disorders using a Polar H10 wearable sensor
<p><strong>ABSTRACT</strong></p> <p>The issue of diagnosing psychotic diseases, including schizophrenia and bipolar disorder, in particular, the objectification of symptom severity assessment, is still a problem requiring the attention of researchers. Two measures that can be helpful in patient diagnosis are heart rate variability calculated based on electrocardiographic signal and accelerometer mobility data. The following dataset contains data from 30 psychiatric ward patients having schizophrenia or bipolar disorder and 30 healthy persons. The duration of the measurements for individuals was usually between 1.5 and 2 hours. R-R intervals necessary for heart rate variability calculation were collected simultaneously with accelerometer data using a wearable Polar H10 device. The Positive and Negative Syndrome Scale (PANSS) test was performed for each patient participating in the experiment, and its results were attached to the dataset. Furthermore, the code for loading and preprocessing data, as well as for statistical analysis, was included on the corresponding GitHub repository.</p> <p><strong>BACKGROUND</strong></p> <p>Heart rate variability (HRV), calculated based on electrocardiographic (ECG) recordings of R-R intervals stemming from the heart's electrical activity, may be used as a biomarker of mental illnesses, including schizophrenia and bipolar disorder (BD) [Benjamin et al]. The variations of R-R interval values correspond to the heart's autonomic regulation changes [Berntson et al, Stogios et al]. Moreover, the HRV measure reflects the activity of the sympathetic and parasympathetic parts of the autonomous nervous system (ANS) [Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology, Matusik et al]. Patients with psychotic mental disorders show a tendency for a change in the centrally regulated ANS balance in the direction of less dynamic changes in the ANS activity in response to different environmental conditions [Stogios et al]. Larger sympathetic activity relative to the parasympathetic one leads to lower HRV, while, on the other hand, higher parasympathetic activity translates to higher HRV. This loss of dynamic response may be an indicator of mental health. Additional benefits may come from measuring the daily activity of patients using accelerometry. This may be used to register periods of physical activity and inactivity or withdrawal for further correlation with HRV values recorded at the same time.</p> <p><strong>EXPERIMENTS</strong></p> <p>In our experiment, the participants were 30 psychiatric ward patients with schizophrenia or BD and 30 healthy people. All measurements were performed using a Polar H10 wearable device. The sensor collects ECG recordings and accelerometer data and, additionally, prepares a detection of R wave peaks. Participants of the experiment had to wear the sensor for a given time. Basically, it was between 1.5 and 2 hours, but the shortest recording was 70 minutes. During this time, evaluated persons could perform any activity a few minutes after starting the measurement. Participants were encouraged to undertake physical activity and, more specifically, to take a walk. Due to patients being in the medical ward, they received instruction to take a walk in the corridors at the beginning of the experiment. They were to repeat the walk 30 minutes and 1 hour after the first walk. The subsequent walks were to be slightly longer (about 3, 5 and 7 minutes, respectively). We did not remind or supervise the command during the experiment, both in the treatment and the control group. Seven persons from the control group did not receive this order and their measurements correspond to freely selected activities with rest periods but at least three of them performed physical activities during this time. Nevertheless, at the start of the experiment, all participants were requested to rest in a sitting position for 5 minutes. Moreover, for each patient, the disease severity was assessed using the PANSS test and its scores are attached to the dataset.</p> <p>The data from sensors were collected using Polar Sensor Logger application [Happonen]. Such extracted measurements were then preprocessed and analyzed using the code prepared by the authors of the experiment. It is publicly available on the GitHub repository [Książek et al].</p> <p>Firstly, we performed a manual artifact detection to remove abnormal heartbeats due to non-sinus beats and technical issues of the device (e.g. temporary disconnections and inappropriate electrode readings). We also performed anomaly detection using Daubechies wavelet transform. Nevertheless, the dataset includes raw data, while a full code necessary to reproduce our anomaly detection approach is available in the repository. Optionally, it is also possible to perform cubic spline data interpolation. After that step, rolling windows of a particular size and time intervals between them are created. Then, a statistical analysis is prepared, e.g. mean HRV calculation using the RMSSD (Root Mean Square of Successive Differences) approach, measuring a relationship between mean HRV and PANSS scores, mobility coefficient calculation based on accelerometer data and verification of dependencies between HRV and mobility scores.</p> <p><strong>DATA DESCRIPTION</strong></p> <p>The structure of the dataset is as follows. One folder, called <em>HRV_anonymized_data</em> contains values of R-R intervals together with timestamps for each experiment participant. The data was properly anonymized, i.e. the day of the measurement was removed to prevent person identification. Files concerned with patients have the name <em>treatment_X.csv</em>, where <em>X</em> is the number of the person, while files related to the healthy controls are named <em>control_Y.csv</em>, where <em>Y</em> is the identification number of the person. Furthermore, for visualization purposes, an image of the raw RR intervals for each participant is presented. Its name is <em>raw_RR_{control,treatment}_N.png</em>, where <em>N</em> is the number of the person from the control/treatment group. The collected data are raw, i.e. before the anomaly removal. The code enabling reproducing the anomaly detection stage and removing suspicious heartbeats is publicly available in the repository [Książek et al]. The structure of consecutive files collecting R-R intervals is following:</p> <table> <tbody> <tr> <td><strong>Phone timestamp</strong></td> <td><strong>RR-interval [ms]</strong></td> </tr> <tr> <td>12:43:26.538000</td> <td>651</td> </tr> <tr> <td>12:43:27.189000</td> <td>632</td> </tr> <tr> <td>12:43:27.821000</td> <td>618</td> </tr> <tr> <td>12:43:28.439000</td> <td>621</td> </tr> <tr> <td>12:43:29.060000</td> <td>661</td> </tr> <tr> <td>...</td> <td>...</td> </tr> </tbody> </table> <p>The first column contains the timestamp for which the distance between two consecutive R peaks was registered. The corresponding R-R interval is presented in the second column of the file and is expressed in milliseconds. <br> The second folder, called <em>accelerometer_anonymized_data</em> contains values of accelerometer data collected at the same time as R-R intervals. The naming convention is similar to that of the R-R interval data: <em>treatment_X.csv </em>and <em>control_X.csv</em> represent the data coming from the persons from the treatment and control group, respectively, while <em>X </em>is the identification number of the selected participant. The numbers are exactly the same as for R-R intervals. The structure of the files with accelerometer recordings is as follows:</p> <table> <tbody> <tr> <td><strong>Phone timestamp</strong></td> <td><strong>X [mg]</strong></td> <td><strong>Y [mg]</strong></td> <td><strong>Z [mg]</strong></td> </tr> <tr> <td>13:00:17.196000</td> <td>-961</td> <td>-23</td> <td>182</td> </tr> <tr> <td>13:00:17.205000</td> <td>-965</td> <td>-21</td> <td>181</td> </tr> <tr> <td>13:00:17.215000</td> <td>-966</td> <td>-22</td> <td>187</td> </tr> <tr> <td>13:00:17.225000</td> <td>-967</td> <td>-26</td> <td>193</td> </tr> <tr> <td>13:00:17.235000</td> <td>-965</td> <td>-27</td> <td>191</td> </tr> <tr> <td>...</td> <td>...</td> <td>...</td> <td>...</td> </tr> </tbody> </table> <p>The first column contains a timestamp, while the next three columns correspond to the currently registered acceleration in three axes: X, Y and Z, in milli-g unit.</p> <p>We also attached a file with the PANSS test scores (<em>PANSS.csv</em>) for all patients participating in the measurement. The structure of this file is as follows:</p> <table> <tbody> <tr> <td><strong>no_of_person</strong></td> <td><strong>PANSS_P</strong></td> <td><strong>PANSS_N</strong></td> <td><strong>PANSS_G</strong></td> <td><strong>PANSS_total</strong></td> </tr> <tr> <td>1</td> <td>8</td> <td>13</td> <td>22</td> <td>43</td> </tr> <tr> <td>2</td> <td>11</td> <td>7</td> <td>18</td> <td>36</td> </tr> <tr> <td>3</td> <td>14</td> <td>30</td> <td>44</td> <td>88</td> </tr> <tr> <td>4</td> <td>18</td> <td>13</td> <td>27</td> <td>58</td> </tr> <tr> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>..</td> </tr> </tbody> </table> <p><br> The first column contains the identification number of the patient, while the three following columns refer to the PANSS scores related to positive, negative and general symptoms, respectively.</p> <p><strong>USAGE NOTES</strong></p> <p>All the files necessary to run the HRV and/or accelerometer data analysis are available on the GitHub repository [Książek et al]. HRV data loading, preprocessing (i.e. anomaly detection and removal), as well as the calculation of mean HRV values in terms of the RMSSD, is performed in the <em>main.py</em> file. Also, Pearson's correlation coefficients between HRV values and PANSS scores and the statistical tests (Levene's and Mann-Whitney U tests) comparing the treatment and control groups are computed. By default, a sensitivity analysis is made, i.e. running the full pipeline for different settings of the window size for which the HRV is calculated and various time intervals between consecutive windows. Preparing the heatmaps of correlation coefficients and corresponding p-values can be done by running the <em>utils_advanced_plots.py</em> file after performing the sensitivity analysis. Furthermore, a detailed analysis for the one selected set of hyperparameters may be prepared (by setting <em>sensitivity_analysis = False</em>), i.e. for 15-minute window sizes, 1-minute time intervals between consecutive windows and without data interpolation method. Also, patients taking quetiapine may be excluded from further calculations by setting <em>exclude_quetiapine = True</em> because this medicine can have a strong impact on HRV [Hattori et al].</p> <p>The accelerometer data processing may be performed using the <em>utils_accelerometer.py</em> file. In this case, accelerometer recordings are downsampled to ensure the same timestamps as for R-R intervals and, for each participant, the mobility coefficient is calculated. Then, a correlation coefficient between mean HRV values and mobility coefficient is computed. The plotting of the pure accelerometer signal may be done by running the <em>utils_loading.py </em>file.</p> <p>The comparison of age distribution between the tested groups can be made by the histogram plotted with the use of the <em>utils_basic_plots.py</em> file.</p>
High frequency limnological sensor data from three lakes in the Pocono Mountains region, Pennsylvania USA, 2016-2024
This dataset publication provides access to eight years of high-frequency sensor data from three lakes: Giles, Lacawac, and Waynewood. These lakes are located in the Pocono Mountains region of Pennsylvania, USA and have been the site of long-term monitoring and research. Lake Giles is a relatively clear-water low dissolved organic matter oligotrophic lake in a largely protected watershed. Lake Lacawac has higher dissolved organic matter concentrations and is considered a dystrophic brown-water system; it is also in a highly-protected watershed. Lake Waynewood is a relatively productive eutrophic lake with a larger watershed that is mixed agricultural, forested, and residential use. High-frequency sensors were deployed on sensor lines at the deepest point in each lake. Measurements included temperature and dissolved oxygen through the water column, fluorescent dissolved organic matter at the surface and bottom, and chlorophyll fluorescence at the surface of each lake. These data are collected at a frequency of 10 to 30 minutes and are available in the data packages GilesHighFrequencyData.csv, LacawacHighFrequencyData.csv, and WaynewoodHighFrequencyData.csv. Data from weather stations located adjacent to each lake can be found in the data package PoconosWeatherStationData.csv. Additional long-term limnological data (four decades) for Lakes Giles, Lacawac, and Waynewood are available in the data package edi.186.8.
Sensor and nutrient data associated with the article Harrison et al. 2020. Prediction of stream nitrogen and phosphorus concentrations from high-frequency sensors using Random Forests Regression
This document describes a dataset used to produce Random Forests Regression models of stream nitrogen and phosphorus concentrations from high-frequency sensor data, as reported in: Harrison, J.W., Lucius, M.A., Farrell, J.L., Eichler, L.W., and Relyea, R.A. 2020. Prediction of stream nitrogen and phosphorus concentrations from high-frequency sensors using Random Forests Regression. Science of the Total Environment: https://doi.org/10.1016/j.scitotenv.2020.143005. The dataset consists of paired values of stream nitrogen and phosphorus concentrations and various high-frequency sensor parameters (water temperature, specific conductance, pH, fluorescent dissolved organic matter, turbidity, hydrostatic pressure, soil moisture) collected during baseflow and storm events from 2018 to 2019 as part of routine monitoring of eleven tributaries of Lake George, New York. This dataset does not include raw data; two levels of processing were performed: (1) erroneous values (extreme or otherwise outlying values with no apparent environmental cause) were removed from the sensor data as part of the routine QA/QC process of the Jefferson Project, and (2) one-hour rolling medians of the raw sensor data were calculated at a 1-minute timestep to maximize pairing of sensor data with nutrient concentrations. The resultant dataset was used to train and test the models presented in Harrison et al. 2020.
Hourly meteorological data gapfilled for sensor downtimes collected near Toolik Field Station, Alaska, summers 2012-2016
This data set includes meteorological parameters collected near Toolik Field Station from 2012 to 2016 under National Science Foundation (NSF) Office of Polar Programs ARC 0908444 (to Laura Gough), ARC 0908602 (to Natalie Boelman), and ARC 0909133 (to John Wingfield). It also includes meteorological data collected by two additional entities that are available on public repositories. Toolik data reflect data collected by the Toolik Envronmental Data Center and Imnavait data reflect data collected by the Arctic Observatory Network (AON). These data have been modified such that that sensor downtimes have been gapfilled by pulling data from the next nearest station. These data are associated with publication DOI: 10.1111/jav.01712
Luquillo canopy trimming experiment - meteorological data, soil sensor, and cannopy measurements
The data archive is here: https://doi.org/10.2737/RDS-2019-0051 please use this DOI when citing this dataset. This data publication contains average daily means for field and satellite data from 3 treated and 3 control plots from the Canopy Trimming Experiment (CTE) located near El Verde Field Station (419 meters; 18°20’ N, 65°49’ W) in the Luquillo Experimental Forest (El Yunque National Forest), Puerto Rico collected from 2003 through 2019. In spring of 2005 (CTE1) and December of 2014 (CTE2), in 0.09 hectare (ha) square plots near the El Verde Field Station the forest canopy was trimmed and the canopy debris was littered to the forest floor. The plot size and trim amounts were based on the patch disturbance after the two most recent hurricanes before 2017, both category 3 hurricanes at the location of El Verde: Hugo in September 1989, and Georges in September 1998 (Zimmerman et al. 2014). Details of the trimming and littering treatment, as well as the biotic response to the 2005 experiment have been extensively documented (Richardson et al. 2010; Shiels et al. 2014, 2015; Shiels and González 2014). The data were collected in the inner 0.04 ha quadrants of the 0.09 ha trimmed plots to minimize edge effects. There were 3 sets of control and treated plots, with each set near El Verde field station. Field data include: solar radiation, throughfall, air temperature, soil temperature, relative humidity shallow soil, volumetric water content, soil profile volumetric water content, canopy leaf saturation, litter leaf saturation, and above canopy air temperature. Satellite data include: Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index converted into solar radiation, MODIS land surface temperature, and Advanced Microwave Scanning Radiometer (AMSR) soil volumetric water content. \<para\> Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952
MCR LTER: Coral Reef: Sensor Network: Bottom-mounted CTD Data - GUMPR, 2006-2012
Physical oceanographic data from bottom-mounted instrumentation (Seabird 16+ CTD) were sampled year-round on Gump reef in Cooks Bay on Moorea, French Polynesia (GUMPR site). Sampling began in 2006 until early 2012. The CTD measured conductivity, temperature, pressure, from which density and salinity were calculated. Data were collected every 5 minutes, processed and reported every 20 minutes. The instrument is mounted 2 m above the bottom in 6 m of depth. These data streamed near real-time as part of the Digital Moorea project (no longer active.) Daily, weekly, monthly and yearly means were calculated for temperature, salinity, and density. This is a completed timeseries which ended early 2012. 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.
Supplemental soil moisture and temperature data from the saddle catchment sensor network, 2019 - 2021.
Hand-held soil moisture measurements were taken at 8 of 16 soil moisture sensors within the sensor network at Niwot Ridge to supplement the continuous measurement system at these locations. The hand-held measurements occur much less frequently than the 10 min sensor data, but they are important in determining the spatial variability of soil moisture in the alpine.
Year 2020, PIE LTER wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA
Wind sensor measurements (wind speed and wind direction) for 2020 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute average measurements.
Year 2021, PIE LTER wind sensor data, 15 minute intervals, from the Ipswich Bay Yacht Club pier located in Ipswich, MA
Wind sensor measurements (wind speed and wind direction) for 2021 at the Ipswich Bay Yacht Club, Ipswich, MA, 15 minute average measurements.
Nearshore DuraFET and HOBO Sensor Data: pH and Temperature (May–Nov 2021)
Data are pH (total scale, DuraFET) and temperature (degrees Celsius, DuraFET and HOBO TidbiT) from instruments deployed at three sites in the rocky intertidal zone in California, USA. pH data were collected every 15 minutes and HOBO temperature data were collected every 5 minutes and interpolated to a 15 minute interval. Sensor data collection began in May 2021 and was terminated in November 2021. DuraFET-based pH sensors were constructed with guidance from Dr. Francis Chan at Oregon State University.
Software for processing data from a fast-responding RINKO EC oxygen/temperature sensor (JFE Advantech Co, Ltd)
This dataset describes how data from a fast-responding JFE Advantech RINKO EC ARO-EC-CM sensor connected to a Nortek Vector is processed to obtain accurate aquatic eddy covariance measurements. The code and documentation are stored in a .zip file. It consists of a manual, Fortran source code, a definition file and a complied executable suitable for running on Microsoft Windows. The software development was supported by NSF funding to PI Berg (OCE-1824144, OCE-2223204).
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>
Data Set of Extracted Summary Statistics from Equipment Sensor Data
<p>This data set was generated in accordance with the semiconductor industry and contains values of summary statistics from sensor recordings of the high-precision and high-tech production equipment. Basically, the semiconductor production consists of hundreds of process steps performing physical and chemical operations on so-called wafers, i.e. slices based on semiconductor material. In the production chain, each process equipment is equipped with several sensors recording physical parameters like gas flow, temperature, voltage, etc., resulting in so-called sensor data. Out of the sensor data, values of summary statistics are extracted. These are values like mean, standard deviation and gradients. To keep the entire production as stable as possible, these values are used to monitor the whole production in order to intervene in case of deviations.</p> <p>After the production, each device on the wafer is tested in the most careful way resulting in so-called wafer test data. In some cases, suspicious patterns occur in the wafer test data potentially leading to failure. In this case the root cause must be found in the production chain. For this purpose, the given data is provided. The aim is to find correlations between the wafer test data and the values of summary statistics in order to identify the root cause.</p> <p>The given data is divided into four data sets: "XTrain.csv", "YTrain.csv", "XTest.csv" and "YTest.csv". "XTrain.csv" and "XTest.csv" represent the values of summary statistics originating in the production chain separated for the purpose of training and validating a statistical model. Included are 114 observations of 77 parameters (values of summary statistics). The "YTrain.csv" and "YTest.csv" contain the corresponding wafer test data (144 observations of one parameter).</p>
Improvement of frequency responses of an in-plane electro-thermal cantilever sensor for real-time measurement (Data)
<p>Origin projects, figures and COMSOL simulation used for the article "Improvement of frequency responses of an in-plane electro-thermal cantilever sensor for real-time measurement", published in <em>Journal of Micromechanics and Microengineering </em>on 05 Nov 2019.</p>
In-Plane and Out-of-Plane MEMS Piezoresistive Cantilever Sensors for Nanoparticle Mass Detection (Data)
<p>Origin projects, figures and LabVIEW software used for the article "In-Plane and Out-of-Plane MEMS Piezoresistive Cantilever Sensors for Nanoparticle Mass Detection", published in <em>Sensors </em>on 22 Jan 2020.</p>
Phase characteristic optimization of resonant MEMS environmental sensors (Data)
<p>Origin projects and figures used for the article "Phase characteristic optimization of resonant MEMS environmental sensors", published in the proceedings of Sensoren und Messsysteme 2018, 19. ITG/GMA-Fachtagung; 26.06.2018 to 27.06.2018; Nürnberg, Germany.</p>
MEMS-Based Cantilever Sensor for Simultaneous Measurement of Mass and Magnetic Moment of Magnetic Particles (Data)
<p>Origin project and figures used for the article "MEMS-Based Cantilever Sensor for Simultaneous Measurement of Mass and Magnetic Moment of Magnetic Particles", published in <em>Chemosensors</em> on 04 Aug 2021.</p>
Ambient air sensor data for 2022-01-17
<p>Raw data from ambient air sensors BME680 for 2022-01-17 at</p> <p>CIE: Centro de Intercambios Escolares (28049 Madrid, Spain)</p> <p>CFA Taller de Naturaleza "Villaviciosa de Odón" (28300 Villaviciosa de Odón, Spain)</p> <p>IES "Luis Cobiella Cuevas" (38700 Santa Cruz de La Palma, Spain)</p> <p>Data begin at 08:00 because station at La Palma is connected daily at that time</p> <p>UNITS:</p> <p>Madrid time (CET - GMT +1)</p> <p>Air temperature T / ºC</p> <p>Relative Humidity RH / %</p> <p>Barometric pressure P / hPa</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.