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8,460 results for “MONITORING”
Snowpack ensemble simulations at the Kühtai snow monitoring station
<p>This dataset presents an ensemble of 230000 point-scale snowpack simulations at the snow monitoring station Kühtai (Tyrol, Austria) over the course of 25 winter seasons. The dataset splits in simulations for various sensitivity analysis designs: i.e. to assess the sensitivity of 1) forcing data error, model structure, and parametrization, 2) different forcing variables while perturbing model structure and parametrization, 3) model structures while perturbing forcing errors and parametrization.</p>
EV data monitoring
<p>Electric Vehicle (EV) data collected by Emotion, expert Italian company specialized in the sector of electric mobility, partner of SOFIE project.</p> <p>These data were collected using a particular OBD device connected on each EV. OBD communicates using a TCP/IP system.</p> <p>Data shown in this dataset were updated every 10 seconds. </p>
EV data monitoring
<p>Electric Vehicle (EV) data collected by Emotion, expert Italian company specialized in the sector of electric mobility, partner of SOFIE project.</p> <p>These data were collected using a particular OBD device connected on each EV. OBD communicates using a TCP/IP system.</p> <p>Data shown in this dataset were updated every 10 seconds. </p>
EV data monitoring
<p>Electric Vehicle (EV) data collected by Emotion, expert Italian company specialized in the sector of electric mobility, partner of SOFIE project.</p> <p>These data were collected using a particular OBD device connected on each EV. OBD communicates using a TCP/IP system.</p> <p>Data shown in this dataset were updated every 10 seconds. </p>
EU Pesticide monitoring data 2016
<p>The file presents the results of the 2016 EU pesticide monitoring activities of EU Member States, Iceland and Norway.</p>
Condition monitoring of hydraulic systems Data Set at ZeMA
<p><strong>Abstract:</strong></p> <p>The data set addresses the condition assessment of a hydraulic test rig based on multi sensor data. Four fault types are superimposed with several severity grades impeding selective quantification.</p> <p> </p> <p><strong>Source:</strong></p> <p>Creator: ZeMA gGmbH, Eschberger Weg 46, 66121 Saarbrücken<br> Contact: t.schneider <strong>'@'</strong> zema.de, s.klein <strong>'@'</strong> zema.de, m.bastuck <strong>'@'</strong> lmt.uni-saarland.de, info <strong>'@'</strong> lmt.uni-saarland.de</p> <p> </p> <p><strong>Data Set Information:</strong></p> <p>The data set was experimentally obtained with a hydraulic test rig. This test rig consists of a primary working and a secondary cooling-filtration circuit which are connected via the oil tank [1], [2]. The system cyclically repeats constant load cycles (duration 60 seconds) and measures process values such as pressures, volume flows and temperatures while the condition of four hydraulic components (cooler, valve, pump and accumulator) is quantitatively varied.</p> <p> </p> <p><strong>Attribute Information:</strong></p> <p>The data set contains raw process sensor data (i.e. without feature extraction) which are structured as matrices (tab-delimited) with the rows representing the cycles and the columns the data points within a cycle. The sensors involved are:<br> Sensor Physical quantity Unit Sampling rate<br> PS1 Pressure bar 100 Hz<br> PS2 Pressure bar 100 Hz<br> PS3 Pressure bar 100 Hz<br> PS4 Pressure bar 100 Hz<br> PS5 Pressure bar 100 Hz<br> PS6 Pressure bar 100 Hz<br> EPS1 Motor power W 100 Hz<br> FS1 Volume flow l/min 10 Hz<br> FS2 Volume flow l/min 10 Hz<br> TS1 Temperature °C 1 Hz<br> TS2 Temperature °C 1 Hz<br> TS3 Temperature °C 1 Hz<br> TS4 Temperature °C 1 Hz<br> VS1 Vibration mm/s 1 Hz<br> CE Cooling efficiency (virtual) % 1 Hz<br> CP Cooling power (virtual) kW 1 Hz<br> SE Efficiency factor % 1 Hz<br> <br> The target condition values are cycle-wise annotated in ‘profile.txt’ (tab-delimited). As before, the row number represents the cycle number. The columns are<br> <br> 1: Cooler condition / %:<br> 3: close to total failure<br> 20: reduced effifiency<br> 100: full efficiency<br> <br> 2: Valve condition / %:<br> 100: optimal switching behavior<br> 90: small lag<br> 80: severe lag<br> 73: close to total failure<br> <br> 3: Internal pump leakage:<br> 0: no leakage<br> 1: weak leakage<br> 2: severe leakage<br> <br> 4: Hydraulic accumulator / bar:<br> 130: optimal pressure<br> 115: slightly reduced pressure<br> 100: severely reduced pressure<br> 90: close to total failure<br> <br> 5: stable flag:<br> 0: conditions were stable<br> 1: static conditions might not have been reached yet</p> <p> </p> <p><strong>Relevant Papers:</strong></p> <p>[1] Nikolai Helwig, Eliseo Pignanelli, Andreas Schütze, ‘Condition Monitoring of a Complex Hydraulic System Using Multivariate Statistics’, in Proc. I2MTC-2015 - 2015 IEEE International Instrumentation and Measurement Technology Conference, paper PPS1-39, Pisa, Italy, May 11-14, 2015, doi: 10.1109/I2MTC.2015.7151267.<br> [2] N. Helwig, A. Schütze, ‘Detecting and compensating sensor faults in a hydraulic condition monitoring system’, in Proc. SENSOR 2015 - 17th International Conference on Sensors and Measurement Technology, oral presentation D8.1, Nuremberg, Germany, May 19-21, 2015, doi: 10.5162/sensor2015/D8.1.<br> [3] Tizian Schneider, Nikolai Helwig, Andreas Schütze, ‘Automatic feature extraction and selection for classification of cyclical time series data’, tm - Technisches Messen (2017), 84(3), 198 – 206, doi: 10.1515/teme-2016-0072.</p> <p><br> </p> <p><strong>Citation Request:</strong></p> <p>Nikolai Helwig, Eliseo Pignanelli, Andreas Schütze, ‘Condition Monitoring of a Complex Hydraulic System Using Multivariate Statistics’, in Proc. I2MTC-2015 - 2015 IEEE International Instrumentation and Measurement Technology Conference, paper PPS1-39, Pisa, Italy, May 11-14, 2015, doi: 10.1109/I2MTC.2015.7151267.</p>
EV Data monitoring
<p>Electric Vehicle (EV) data collected by Emotion, expert Italian company specialized in the sector of electric mobility, partner of SOFIE project.</p> <p>These data were collected using a particular OBD device connected on each EV. OBD communicates using a TCP/IP system.</p> <p>Data shown in this dataset were updated every 10 seconds. </p>
Snowpack ensemble simulation at the Kühtai snow monitoring station
<p>This dataset presents an ensemble of 230000 point-scale snowpack simulations at the snow monitoring station Kühtai (Tyrol, Austria) over the course of 25 winter seasons. We provide time series of simulated snow water equivalent, errors of these simulations and the corresponding sensitivity indices. The dataset splits in simulations for various sensitivity analysis designs: i.e. to assess the sensitivity of a) forcing data error, model structure, and parametrization, b) different forcing variables while perturbing model structure and parametrization, c) model structures while perturbing forcing errors and parametrization.</p>
Monitoring of the Chillon viaduct after strengthening with UHPFRC
<p>Vibration data from accelerometers installed in Chillon Viaduct. Accelerometers positions included in the pdf. sampling freqeuncy= 200Hz. </p>
Data for: Beyond signal quality: The value of unmaintained pH, dissolved oxygen, and oxidation-reduction potential sensors for remote performance monitoring of on-site sequencing batch reactors
<p>Sensor maintenance is time-consuming and is a bottleneck for monitoring on-site wastewater treatment systems. Hence, we compare maintained and unmaintained sensors to monitor the biological performance of a small-scale sequencing batch reactor (SBR). The sensor types are ion-selective pH, optical dissolved oxygen (DO), and oxidation-reduction potential (ORP) with platinum electrode. We created soft sensors using engineered features: ammonium valley for pH, oxidation ramp for DO, and nitrite ramp for the ORP. Four soft sensors based on unmaintained pH sensors correctly identified the completion of the ammonium oxidation (89 to 91 out of 107 cycles), about as many times as soft sensors based on a maintained pH sensor (91 out of 107 cycles). In contrast, the DO soft sensor using data from a maintained sensor showed slightly better (89 out of 96 cycles) detection performance than that using data from two unmaintained sensors (77, respectively 82 out of 96 correct). Furthermore, the DO soft sensor using maintained data is much less sensitive to the optimisation of cut-off frequency and slope tolerance than the soft sensor using unmaintained data. The nitrite ramp provided no useful information on the state of nitrite oxidation, so no comparison of maintained and unmaintained ORP sensors was possible in this case. We identified two hurdles when designing soft sensors for unmaintained sensors: i) Sensors' type- and design-specific deterioration affects performance. ii) Feature engineering for soft sensors is sensor type specific, and the outcome is strongly influenced by operational parameters such as the aeration rate. In summary, the results with the provided soft sensors show that frequent sensor maintenance is not necessarily needed to monitor the performance of SBRs. Without sensor maintenance monitoring smalls-scale SBRs becomes practicable, which could improve the reliability of unstaffed on-site treatment systems substantially.</p>
EU Pesticide monitoring data 2017
<p>The file presents the results of the 2017 EU pesticide monitoring activities of EU Member States, Iceland and Norway.</p>
Outdoor monitoring of a hybrid micro-CPV solar panel with integrated micro-tracking and diffuse capture
<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. These are measurements of a module of the same type as “<a href="zenodo.org/record/2667772">Outdoor monitoring data of an Insolight B-series module - CPV sub-module</a>” however, in the previous measurements the module was mounted on a two-axis tracker to benchmark its performance, while in these measurements, the module’s integrated planar micro tracking system was used. This data was presented at IEEE PVSC 46 in June 2019 in Chicago. <strong><a href="https://zenodo.org/record/3349781">See preprint of conference article</a>.</strong></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>Fixed Mounting Angle: Due South, Slope Angle = 30°</li> <li>Starting date: 30 May 2019</li> <li>End date: 14 June 2018</li> </ul> <p><strong>Description of data file:</strong></p> <ul> <li><strong>Data files format:</strong> single comma-separated text file; headers in first row; all of the following parameters; order below is the same as order in file</li> <li><strong>Measurement time</strong>: the vector of times represents the times at which the Insolight module firmware sampled the current values of the III-V and Si outputs (measured simultaneously). <ul> <li>Date Time (dd/mmm/yyyy HH:MM:SS): time in CEST / UTC+2</li> </ul> </li> <li><strong>Measured meteorological data</strong>: these values are measured directly by the IES meteorological station with 1-minute resolution. They have been re-interpolated to match the measurement times. <ul> <li>DNI (W/m2): direct normal irradiance as measured by a Normal Incidence Pyrheliometer from Eppley on a solar tracker. Spectral Range: 250-3000 nm. Field of view: 5°</li> <li>DNI_Top (W/m2): equivalent direct normal irradiance as measured by a top component cell of a lattice-matched III-V triple-junction cell in the ICU-3J35 Triband Spectro-heliometer from Solar Added Value on a solar tracker. Spectral range: 300 - 680 nm. Field of view: 5.7º</li> <li>DNI_Mid (W/m2): equivalent 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> Triband Spectro-heliometer from Solar Added Value on a solar tracker. Spectral range: 680 - 900 nm. Field of view: 5.7º</li> <li>GNI (W/m2): global normal irradiance at the aperture plane as measured with a pyranometer on a solar tracker. Spectral range: 305 – 2800 nm.</li> <li>G(41°) (W/m2): Global Inclined Irradiance as measured with a pyranometer mounted facing due south and at a slope angle of 41° (near to local latitude). Spectral range: 305 – 2800 nm.</li> <li>T_Amb (°C): ambient temperature </li> <li>Wind Speed (m/s): wind speed</li> <li>Wind Dir. (m/s): wind direction</li> </ul> </li> <li><strong>Processed meteorological data</strong>: these values are calculated from the above meteorological data and provided for convenience <ul> <li>DII (W/m2): Direct Inclined (plane of array) Irradiance corresponding to the module slope angle has been calculated using the sun’s known declination and hour angle from the time.</li> <li>GII (W/m2): The Global Inclined (plane of array) Irradiance is calculated by first calculating the DII(41°), that is the DII corresponding to the G(41°) measurement, and finding the Diffuse Inclined Irradiance Diff(41°) = G(41°) – DII(41°). It is assumed that the Diffuse Inclined Irradiance at 41° and 30° is equal, so GII = DII + Diff(41°).</li> <li>SMR_Top_Mid (n.d.): “Spectral Matching Ratio”. This is the ratio between DNI_Top and DNI_Mid. A value of unity indicates a spectrum that is equivalent to AM1.5D with regards to the energy balance between top and middle subcells.</li> </ul> </li> <li><strong>Measured module data:</strong> The module was placed in a short-ciruit condition and allowed to track using its integrated tracking system. The short circuit current was measured using shut resistors and integrated A/D channels. This hybrid module features both III-V micro cells (under concentration, with planar microtracking) and large area silicon solar cells (for diffuse capture). <ul> <li>ISC_measured_IIIV (A):</li> <li>ISC_measured_Si (A)</li> </ul> </li> <li><strong>Estimated module data:</strong> As is explained in the IEEE PVSC 46 manuscript (<a href="https://zenodo.org/record/3349781">see Preprint</a>) the following values are estimated using the previously listed measured data. <ul> <li>T_Backplane (°C)</li> <li>PMP_estimated_IIIV (W)</li> <li>PMP_estimated_Si (W)</li> </ul> </li> </ul>
Bioacoustic monitoring reveals shifts in breeding songbird populations and singing behaviour with selective logging in tropical forests
<b>Description: </b><p>Counts of individual male songbirds, males and females, songs and duets and original WAV audio recordings used to generate them</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/131"><b>Population and behavioral responses of songbirds to logging and rain forest fragmentation</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3366104">here</a></p><p><b>Files: </b>This dataset consists of 13 files: Pillay_et_al_Songbirds_Acoustic_Counts_Vegetation_Cover.xlsx, 2013_B.zip, 2013_D.zip, 2013_E.zip, 2013_F.zip, 2013_OG1.zip, 2013_OG2.zip, 2014_B.zip, 2014_D.zip, 2014_E.zip, 2014_F.zip, 2014_OG1.zip, 2014_OG2.zip</p><p><b>Pillay_et_al_Songbirds_Acoustic_Counts_Vegetation_Cover.xlsx</b></p><p>This file contains dataset metadata and 5 data tables:</p><ol><li><p><b>CountsMale</b> (described in worksheet CountsMale)</p><p>Description: Counts of male individuals of songbird species</p><p>Number of fields: 12</p><p>Number of data rows: 5700</p><p>Fields: </p><ul><li><b>year</b>: Year of Survey (Field type: id)</li><li><b>ftype</b>: Forest Type (Field type: categorical)</li><li><b>block</b>: Unique ID of Blocks within which Sampling Plots are located (Field type: id)</li><li><b>location</b>: Unique ID of Sampling Plots (Field type: location)</li><li><b>fragment</b>: Future fragments within which sampling plots in logged forest plots were located; not applicable for unlogged forest plots (Field type: id)</li><li><b>species</b>: Species Identity (Field type: taxa)</li><li><b>day</b>: Days 1 to 2 of sampling in each plot (Field type: numeric)</li><li><b>date</b>: Date of Sampling (Field type: date)</li><li><b>jul.date</b>: Julian Date of Sampling (Field type: numeric)</li><li><b>time1-6AM</b>: Counts of male individuals for 6:00-6:05 AM; detection histories generated by collapsing counts to 1/0 data (Field type: numeric)</li><li><b>time2-7AM</b>: Counts of male individuals for 7:00-7:05 AM; detection histories generated by collapsing counts to 1/0 data (Field type: numeric)</li><li><b>time3-8AM</b>: Counts of male individuals for 8:00-8:05 AM; detection histories generated by collapsing counts to 1/0 data (Field type: numeric)</li></ul></li><li><p><b>CountsMaleFemale</b> (described in worksheet CountsMaleFemale)</p><p>Description: Counts of male plus female individuals of songbird species</p><p>Number of fields: 12</p><p>Number of data rows: 1000</p><p>Fields: </p><ul><li><b>year</b>: Year of Survey (Field type: id)</li><li><b>ftype</b>: Forest Type (Field type: categorical)</li><li><b>block</b>: Unique ID of Blocks within which Sampling Plots are located (Field type: id)</li><li><b>location</b>: Unique ID of Sampling Plots (Field type: location)</li><li><b>fragment</b>: Future fragments within which sampling plots in logged forest plots were located; not applicable for unlogged forest plots (Field type: id)</li><li><b>species</b>: Species Identity (Field type: taxa)</li><li><b>day</b>: Days 1 to 2 of sampling in each plot (Field type: numeric)</li><li><b>date</b>: Date of Sampling (Field type: date)</li><li><b>jul.date</b>: Julian Date of Sampling (Field type: numeric)</li><li><b>time1-6AM</b>: Counts of male and female individuals for 6:00-6:05 AM; detection histories generated by collapsing counts to 1/0 data (Field type: numeric)</li><li><b>time2-7AM</b>: Counts of male and female individuals for 7:00-7:05 AM; detection histories generated by collapsing counts to 1/0 data (Field type: numeric)</li><li><b>time3-8AM</b>: Counts of male and female individuals for 8:00-8:05 AM; detection histories generated by collapsing counts to 1/0 data (Field type: numeric)</li></ul></li><li><p><b>CountsSong</b> (described in worksheet CountsSong)</p><p>Description: Counts of songs</p><p>Number of fields: 9</p><p>Number of data rows: 2850</p><p>Fields: </p><ul><li><b>year</b>: Year of Survey (Field type: id)</li><li><b>ftype</b>: Forest Type (Field type: categorical)</li><li><b>block</b>: Unique ID of Blocks within which Sampling Plots are located (Field type: id)</li><li><b>location</b>: Unique ID of Sampling Plots (Field type: location)</li><li><b>fragment</b>: Future fragments within which sampling plots in logged forest plots were located; not applicable for unlogged forest plots (Field type: id)</li><li><b>species</b>: Species Identity (Field type: taxa)</li><li><b>day3-6AM</b>: Counts of songs for 6:00-6:05 AM on day 3 or third consecutive day of sampling in each plot (Field type: numeric)</li><li><b>day3-7AM</b>: Counts of songs for 7:00-7:05 AM on day 3 or third consecutive day of sampling in each plot (Field type: numeric)</li><li><b>day3-8AM</b>: Counts of songs for 8:00-8:05 AM on day 3 or third consecutive day of sampling in each plot (Field type: numeric)</li></ul></li><li><p><b>CountsDuet</b> (described in worksheet CountsDuet)</p><p>Description: Counts of duets</p><p>Number of fields: 9</p><p>Number of data rows: 500</p><p>Fields: </p><ul><li><b>year</b>: Year of Survey (Field type: id)</li><li><b>ftype</b>: Forest Type (Field type: categorical)</li><li><b>block</b>: Unique ID of Blocks within which Sampling Plots are located (Field type: id)</li><li><b>location</b>: Unique ID of Sampling Plots (Field type: location)</li><li><b>fragment</b>: Future fragments within which sampling plots in logged forest plots were located; not applicable for unlogged forest plots (Field type: id)</li><li><b>species</b>: Species Identity (Field type: taxa)</li><li><b>day3-6AM</b>: Counts of duets for 6:00-6:05 AM on day 3 or third consecutive day of sampling in each plot (Field type: numeric)</li><li><b>day3-7AM</b>: Counts of duets for 7:00-7:05 AM on day 3 or third consecutive day of sampling in each plot (Field type: numeric)</li><li><b>day3-8AM</b>: Counts of duets for 8:00-8:05 AM on day 3 or third consecutive day of sampling in each plot (Field type: numeric)</li></ul></li><li><p><b>VegetationCover</b> (described in worksheet VegetationCover)</p><p>Description: Vegetation cover data</p><p>Number of fields: 6</p><p>Number of data rows: 50</p><p>Fields: </p><ul><li><b>location</b>: Unique ID of Sampling Plots (Field type: location)</li><li><b>forest.type</b>: Forest Type (Field type: categorical)</li><li><b>udens</b>: Proportion understory cover (Field type: numeric)</li><li><b>cc</b>: Proportion canopy cover (Field type: numeric)</li><li><b>can.ht</b>: Average canopy height (Field type: numeric)</li><li><b>max.canopy</b>: Maximum height of standing vegetation (Field type: numeric)</li></ul></li></ol><p><b>2013_B.zip</b></p><p>Description: WAV files from 2013 for site B</p><p><b>2013_D.zip</b></p><p>Description: WAV files from 2013 for site D</p><p><b>2013_E.zip</b></p><p>Description: WAV files from 2013 for site E</p><p><b>2013_F.zip</b></p><p>Description: WAV files from 2013 for site F</p><p><b>2013_OG1.zip</b></p><p>Description: WAV files from 2013 for site OG1</p><p><b>2013_OG2.zip</b></p><p>Description: WAV files from 2013 for site OG2</p><p><b>2014_B.zip</b></p><p>Description: WAV files from 2014 for site B</p><p><b>2014_D.zip</b></p><p>Description: WAV files from 2014 for site D</p><p><b>2014_E.zip</b></p><p>Description: WAV files from 2014 for site E</p><p><b>2014_F.zip</b></p><p>Description: WAV files from 2014 for site F</p><p><b>2014_OG1.zip</b></p><p>Description: WAV files from 2014 for site OG1</p><p><b>2014_OG2.zip</b></p><p>Description: WAV files from 2014 for site OG2</p><p><b>Date range: </b>2013-04-09 to 2014-07-26</p><p><b>Latitudinal extent: </b>4.6881 to 4.7530</p><p><b>Longitudinal extent: </b>116.9477 to 117.6249</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div> -  Animalia <br> -  -  Chordata <br> -  -  -  Aves <br> -  -  -  -  Passeriformes <br> -  -  -  -  -  Timaliidae <br> -  -  -  -  -  -  <i>Stachyris</i> <br> -  -  -  -  -  -  -  <i>Stachyris maculata</i> <br> -  -  -  -  -  -  -  <i>Stachyris erythroptera</i> <br> -  -  -  -  -  -  -  <i>Stachyris poliocephala</i> <br> -  -  -  -  -  -  <i>Macronus</i> <br> -  -  -  -  -  -  -  <i>Macronus bornensis</i> <br> -  -  -  -  -  -  -  <i>Macronus ptilosus</i> (as synonym: <i>Macronous ptilosus</i>)<br> -  -  -  -  -  -  <i>Stachyridopsis</i> <br> -  -  -  -  -  -  -  <i>Stachyridopsis rufifrons</i> (as synonym: <i>Stachyris rufifrons</i>)<br> -  -  -  -  -  -  <i>Pomatorhinus</i> <br> -  -  -  -  -  -  -  <i>Pomatorhinus montanus</i> <br> -  -  -  -  -  Pellorneidae <br> -  -  -  -  -  -  <i>Trichastoma</i> <br> -  -  -  -  -  -  -  <i>Trichastoma bicolor</i> <br> -  -  -  -  -  -  <i>Alcippe</i> <br> -  -  -  -  -  -  -  <i>Alcippe brunneicauda</i> <br> -  -  -  -  -  -  <i>Pellorneum</i> <br> -  -  -  -  -  -  -  <i>Pellorneum capistratum</i> <br> -  -  -  -  -  -  <i>Malacocincla</i> <br> -  -  -  -  -  -  -  <i>Malacocincla malaccensis</i> <br> -  -  -  -  -  -  <i>Malacopteron</i> <br> -  -  -  -  -  -  -  <i>Malacopteron magnirostre</i> <br> -  -  -  -  -  -  -  <i>Malacopteron magnum</i> <br> -  -  -  -  -  -  -  <i>Malacopteron cinereum</i> <br> -  -  -  -  -  -  -  <i>Malacopteron affine</i> <br> -  -  -  -  -  Pycnonotidae <br> -  -  -  -  -  -  <i>Alophoixus</i> <br> -  -  -  -  -  -  -  <i>Alophoixus bres</i> <br> -  -  -  -  -  -  -  <i>Alophoixus phaeocephalus</i> <br> -  -  -  -  -  -  <i>Tricholestes</i> <br> -  -  -  -  -  -  -  <i>Tricholestes criniger</i> <br> -  -  -  -  -  -  <i>Iole</i> <br> -  -  -  -  -  -  -  <i>Iole olivacea</i> <br> -  -  -  -  -  -  <i>Pycnonotus</i> <br> -  -  -  -  -  -  -  <i>Pycnonotus atriceps</i> <br> -  -  -  -  -  -  -  <i>Pycnonotus simplex</i> <br> -  -  -  -  -  -  -  <i>Pycnonotus eutilotus</i> <br> -  -  -  -  -  -  -  <i>Pycnonotus brunneus</i> <br> -  -  -  -  -  -  -  <i>Pycnonotus erythropthalmos</i> <br> -  -  -  -  -  Stenostiridae <br> -  -  -  -  -  -  <i>Culicicapa</i> <br> -  -  -  -  -  -  -  <i>Culicicapa ceylonensis</i> <br> -  -  -  -  -  Muscicapidae <br> -  -  -  -  -  -  <i>Cyornis</i> <br> -  -  -  -  -  -  -  <i>Cyornis superbus</i> <br> -  -  -  -  -  -  -  <i>Cyornis unicolor</i> <br> -  -  -  -  -  -  <i>Rhinomyias</i> <br> -  -  -  -  -  -  -  <i>Rhinomyias umbratilis</i> <br> -  -  -  -  -  -  <i>Trichixos</i> <br> -  -  -  -  -  -  -  <i>Trichixos pyrropygus</i> <br> -  -  -  -  -  -  <i>Copsychus</i> <br> -  -  -  -  -  -  -  <i>Copsychus stricklandii</i> <br> -  -  -  -  -  Monarchidae <br> -  -  -  -  -  -  <i>Terpsiphone</i> <br> -  -  -  -  -  -  -  <i>Terpsiphone paradisi</i> <br> -  -  -  -  -  -  <i>Hypothymis</i> <br> -  -  -  -  -  -  -  <i>Hypothymis azurea</i> <br></div><p></p>
A Comprehensive Reliability-Based Framework for Corrosion Damage Monitoring and Repair Design of Reinforced Concrete Structures
<p>Corresponding data set for Tran-SET Project No. 17STLSU03. Abstract of the final report is stated below for reference:</p> <p>"In this work, we developed a comprehensive framework for corrosion management of reinforced concrete (RC) structures. This framework includes critical steps of an effective approach to quantify the damage evolution as well as providing the timeframe for effective maintenance/repair strategies for corrosion assessment in RC structures. The framework included several activities including the use of indirect and direct inspection tools, theoretical development for damage prediction, experimental measurements and theoretical development of repair time based on reliability. The uniqueness of the framework is the integration of deterministic modeling of corrosion damage evolution by using mechanistic analysis with statistical modeling on corrosion of RC structures by using measurements from the field (or natural environment) and experimental testing (in the laboratory). The framework includes a simple algorithm that relies in each development and task generated from this work to monitor and estimate the status of the reinforced concrete structures and the most suitable strategy to extend the life of the system while maximizing the reliability."</p>
Development of a Self-Powered Structural Health Monitoring System for Transportation Infrastructure
<p>Corresponding data set for Tran-SET Project No. 17PTAM03. Abstract of the final report is stated below for reference:</p> <p>"Roadways and bridges play an important role in the economic and social health of society by connecting commerce and people. Economic growth and population expansion pose considerable burden on the aging infrastructure (i.e., pavements and bridges). There is a pressing need to develop structural health monitoring (SHM) technologies capable of collecting infrastructure utilization data. Doing so inexpensively with self-powered systems will revolutionize infrastructure monitoring technology, and will improve decision making enabling roadway and bridge preservation. In this study, a self-powered battery-less structural health monitoring (SHM) system was developed. It is powered by a thermal energy harvester equipped with thermoelectric generators (TEGs) driven by temperature differentials between the top of asphalt pavements and their lower layers. An innovative 2-tier TEG harvester was designed to limit the downtime of the SHM system when the temperature differentials are insufficient to power a single unit. The 2-tier system requires a minimum of 2.1⁰C in temperature differential to generate the minimum of 40 mV needed to power the SHM system. The SHM system consists of a DC-DC booster to increase the voltage generated by the harvester, a buck controller to bring this voltage down to the 3.3 Volts required for powering the microcontroller, a microcontroller and a wireless transceiver for transmitting the data. Another transceiver carried on-board a pilot vehicle is needed to retrieve the data. Software was developed in this project to allow data communication between the two transceivers. The SHM system developed accepts analogue voltage input from any sensor that generates analogue voltage, (e.g., piezoelectric axle load sensors, strain gauges, temperature gauges and so on). A prototype of this SHM system was constructed and tested in the lab and it is ready for field implementation."</p>
Integrated Health Monitoring and Reinforcement of Transportation Structures with Optimized Low-Cost Multifunctional Braided Cables
<p>Corresponding data set for Tran-SET Project No. 17STTAM04. Abstract of the final report is stated below for reference:</p> <p>"The objective of this research study is to design, fabricate, and characterize multifunctional high strength and self-sensing braided cables and structures using novel Fe-based shape memory alloys (SMAs). The system exploits unique properties of recently developed low-cost super-elastic FeMnAlNi SMAs, which enables excellent super-elastic properties, high strength, and self-sensing in structural health monitoring (SHM) systems. This novel material technology can be coupled with modeling efforts that allow for accurate prediction of both the materials and structural response during sensing. At the conclusion of the project, we have demonstrated that with careful design of processing parameters, it is possible to control the yield strength and superleastic properties of FeMnAlNi SMAs. We have fabricated, for the first time in the world, a large diameter wires and bars from these inexpensive iron based SMAs which are expected to help with scaling up the fabrication of these materials. In addition, we were able to develop a method for fabricating braided cables from the fabricated Fe-SMA wires and a lab scale experimental setup has been designed and built. Prototype design for the braiding weave was created and tested. Finally, an experimental setup has been designed and manufactured to measure changes in the magnetic response of the SMA braided cables under load in order to directly correlate the magnetic response with deflection and strain. Clearly, the successful fabrication of wires and braided cables of the inexpensive iron based SMAs with superelastic strains comparable to nickel-titanium SMAs and with favorable magnetic sensing capabilities have significant implications for transportation infrastructure as these materials can provide structural health monitoring capability while also mitigating large shape changes during natural disasters. Future work needs to focus on revealing the coupling between mechanical properties (in particular damage) and the changes in magnetic properties in order to provide guidance on how these materials can be utilized in structural health monitoring."</p>
Development, Training, Education, and Implementation of Low-Cost Sensing Technologies for Bridge Structural Health Monitoring (SHM)
<p>Corresponding data set for Tran-SET Project No. 17STUNM02. Abstract of the final report is stated below for reference:</p> <p>"Transportation infrastructure needs continuous monitoring. However, traditional inspections cost money and are conducted visually. New technologies for bridge monitoring are expensive and complex. This project involved developing cost-effective sensor technologies that can be applied towards the maintenance of railroad bridges by recording reference-free transverse displacement. More specifically, this project developed new applications of new technologies (Arduino, wireless smart sensors, drones, Hololens) and promoted workforce development with an emphasis on outreach of high-school students. This project was carried out in three main phases: (1) development and validation of technologies, (2) education and outreach to students, and (3) outreach to industry consisting in one professional workshop. The findings from the first phase showed that the data gathered by these new low-cost sensing systems were comparable to the data collected using traditional sensors. Researchers collected the findings of the second phase of the project through surveys conducted from Middle school, High school and college students during and after outreach activities. these surveys showed that many of the participant students got more interested in the use of new technologies after getting familiar with them. Finally, researchers collected the findings of the third phase of the project through a workshop collecting the interest and challenges of the owners of railroad infrastructure. The top interest of railroad owners is to explore the use of new technologies to increase safety in the field. The conclusions of this research include prioritization on developing low-cost technologies that can measure simple parameters in the field of interest to existing inspectors."</p>
Data repository of the paper "The First Terrestrial Electron Beam Observed by The Atmosphere-Space Interactions Monitor" by D. Sarria et al.
<p>Data repository / Supporting information of the paper "The First Terrestrial Electron Beam Observed by The Atmosphere-Space Interactions Monitor" (2019) by D. Sarria et al.</p> <p>Access to the article: <a href="https://doi.org/10.1029/2019JA027071">https://doi.org/10.1029/2019JA027071</a></p>
Research data supporting "Renal clearable catalytic gold nanoclusters for in vivo disease monitoring"
<p>Raw research data suppoorting the publication:</p> <p>Loynachan C., et al., 2019, Nature Nanotechnology, DOI: https://doi.org/10.1038/s41565-019-0527-6</p>
Data set used in "Diving into science and conservation recreational divers can monitor reef assemblages"
<p>This data set was used in the manuscript "Diving into science and conservation: recreational divers can monitor reef assemblages", accepted for publication in Perspectives in Ecology and Conservation. It is composed by reef species (fishes and turtles) counts in different size classes performed by recreational and experience scientific divers following a sampling protocol. Additionally there is information about recreational divers diving experience, underwater waste register and the scores for their evaluation about conducting scientific data collection.</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.