Find research datasets worth reusing

SHM - Structural Health Monitoring Dataset

Synthetic and Real-World Data as described in C. Kralovec, B. Lehner, M. Kirchmayr, M. Schagerl, "Sandwich Face Layer Debonding Detection and Size Estimation by Machine Learning-based evaluation of Electromechanical Impedance Measurements", Sensors, 2023   The implementation of the experiments can be found here (Python): https://github.com/berni-lehner/structural_health_monitoring

InSAR Time Series Analysis (2018-2021) for Volcanic Monitoring in Northern Chile

This dataset is for the paper "First onset of unrest captured at Socompa: A Recent Geodetic Survey at Central Andean volcanoes in Northern Chile" which is published in GRL: <a href="https://doi.org/10.1029/2022GL102480">https://doi.org/10.1029/2022GL102480</a>. InSAR Data: The folder of InSAR_149A.rar stores the InSAR time series analysis dataset on ascending track 149. <ol> <li>The 'Imagedate' folder stores the empty *.rslc files to indicate the date of each SLCs.</li> <li>Data_Asc.mat stores the main InSAR time series data, which includes the UTC time of the acquisition (for accurate time calculation), the length of perpendicular baselines (unit is meter), the number of days counting from the first epoch, the unwrapped time series data (ifg), the unwrapped time series data with GACOS correction (ifg_aps), the look angles (la, unit is rad), and lat&lon.</li> <li>parms.mat stores the parameters used during the data processing by StaMPS.</li> <li>semi_fit.mat stores the results of the semi-variogram fitting of each interferogram on time series. It provides two versions for the original dataset (semi) and the GACOS-corrected dataset (semi_aps). This file is mainly used to weight the data during the time series fitting.</li> <li>runTSA.m, the main function to run the InSAR time series fitting. See more details in the Code part.</li> </ol> The folder of InSAR_156D.rar stores the same content as the InSAR_149A.rar but for descending track 156. Code: This folder contains the codes of the InSAR time series fitting for this dataset, and the GBIS software. <ol> <li>TSA_findref.m, this function is used to search the reference point of the InSAR data.</li> <li>TSA_EQ_fit.m, is the main function to perform InSAR time series fitting.</li> <li>rb_pixel_fit.m, is the robust way to fit the linear model.</li> <li>TSA_EQ_pixel.m, is the function used to plot the results.</li> </ol> To perform the InSAR time series fitting, you need to put these four functions under your Matlab path, and then run the runTSA.m function in the data folder. The GBIS folder stores the updated version of the GBIS software, which allows you to perform the pCDM, CDM, and pECM. The core functions of these models are provided by Dr. Mehdi Nikkhoo, and you could find them here: https://www.volcanodeformation.com/software GBIS_Modelling_Results: This folder stores the data of InSAR and GPS joint inversion for Socompa Uplift. <ol> <li>The folder Socompa stores the modelling results using the models of Okada(D), pECM(E), Mogi(M), pCDM(N), and Yang(Y), respectively. </li> <li>GPS_data.txt stores the cumulative displacements and the uncertainties of the SOCM station in three directions.</li> <li>Socompa.inp is the configuration file for GBIS running.</li> <li>Vol_asc.mat and Vol_asc_ds.mat stores the original and the downsampled ascending data, while Vol_dsc.mat and Vol_dsc_ds.mat store those of descending.</li> </ol> Many thanks for using our dataset and please let me know if you have any further questions!

opencc-by-4.0Feb 2023View details →

Automated monitoring of biodiversity in the tropics: A pilot study at Barro Colorado Island

Automated monitoring of biodiversity using camera systems and acoustic devises is becoming increasingly common practise. The development of camera traps for insects, however, is relatively new, and has not been tested in tropical environments. To understand both the challenges and opportunities for automated monitoring in the tropics we deployed three newly developed cameras systems for monitoring insects, with a focus on night-flying insects. In addition we deployed audible and ultrasound recording equipment. The locations of each device was changed over the 5 day pilot study, and the configuration of each system was changed to allow an assessment of the relative importance of position, light, etc. The study was undertaken on Barro Colorado Island, Panama, in January 2023. Data are aggregated into .zip files, and details of their contents, and metadata are given in read_me.txt and metadata.csv.

The Effect of Soundscape Composition on Bird Vocalization Classification in a Citizen Science Biodiversity Monitoring Project

This archive includes sound clips (.wav files) and associated mel-scale spectrograms of bird vocalizations for 54 species in Sonoma County, California, USA. These data were used for training and validating convolutional neural network (CNN) models for bird species detection. We also include xeno-canto training and validation mel spectrograms used to pretrain CNNs. Details on these data are explained in the paper by Clark et al. (2023) titled "The effect of soundscape composition on bird vocalization classification in a citizen science biodiversity monitoring project". These data are available for use without restrictions, with no warranty on data quality or utility for a given application. We request that any work that does use these data cite the Clark et al. (2023) paper. Clark, M.L., Salas, L., Baligar, S., Quinn, C., Snyder, R.L., Leland, D., Schackwitz, W., Goetz, S.J., Newsam, S. (2023). The effect of soundscape composition on bird vocalization classification in a citizen science biodiversity monitoring project. Ecological Informatics. <a href="https://doi.org/10.1016/j.ecoinf.2023.102065">https://doi.org/10.1016/j.ecoinf.2023.102065</a> Associated code for training CNN models, performing inference, and applying post-classification corrections can be found in the GitHub archive <a href="https://github.com/pointblue/Soundscapes2Landscapes/tree/master/CNN_Bird_Species">https://github.com/pointblue/Soundscapes2Landscapes/tree/master/CNN_Bird_Species</a> Raw sound data from the Soundscapes to Landscapes project are available upon request: Dr. Matthew Clark, matthew.clark@sonoma.edu These data were collected as part of the Soundscapes to Landscapes project (<a href="https://soundscapes2landscapes.org/">soundscapes2landscapes.org</a>), funded by NASA’s Citizen Science for Earth Systems Program (CSESP) 16-CSESP 2016-0009 under cooperative agreement 80NSSC18M0107. ---------------------------- This depository includes the following archives: <ul> <li> mel_specs.zip: contains 2-sec mel spectrograms split into training (“tr”), validation (“val”), testing (“test”) data for each target bird species (n = 54) used to fine-tune the CNNs. Select spectrogram files are appended with “aug” if they are augmented versions for the training data. </li> <li> wav.zip: contains the associated wav-format sound recordings used to generate the training, validation, testing mel spectrograms found in mel_specs.zip. </li> <li> Xeno-canto_pretrain.tar: contains 2-sec mel spectrograms split into training and validation data for 40 bird species used for CNN pre-training that were generated using a warbleR segmentation methodology described in the paper. The sound files used to generate these mel spectrograms came from the Kaggle competition, <a href="https://www.kaggle.com/datasets/imoore/xenocanto-bird-recordings-dataset">https://www.kaggle.com/datasets/imoore/xenocanto-bird-recordings-dataset</a> Mel spectrogram naming reflects the XC number used for cataloging on Xeno-canto in the format XC123456_2.png. The six numbers following the XC characters can be used to search for unique recordings on Xeno-canto (<a href="https://xeno-canto.org/">https://xeno-canto.org/</a>) using the search query “nr:123456” in the search tool or queried using the Xeno-canto API (<a href="https://xeno-canto.org/explore/api">https://xeno-canto.org/explore/api</a>). Unique recording names can be extracted from the mel spectrogram filenames. </li> <li> soundscape_test_wavs.zip: the wav-format sound recordings used to perform soundscape testing. </li> </ul>

opencc-by-4.0Nov 2022View details →

Pesticide residue monitoring in South Tyrol (Italy)

<ul> <li>Pesticide residue monitoring programm in South Tyrol (Province Bolzano, Italy)</li> <li>The programm is organized and conducted by the Department of Public Health, Servizio Igiene e Sanità Pubblica—Azienda Sanitaria dell’Alto Adige, Bolzano, Italy</li> <li>Further despription and data available at: <ul> <li>https://www.sabes.it/gesundheitsvorsorge/umweltmedizin.asp</li> <li>https://umwelt.provinz.bz.it/pestizide-suedtirol.asp</li> </ul> </li> <li>Residue concentrations of grass samples per sampling site and sampling date can be found in the file "Pesticide_monitoring_SouthTyrol_2018_2021.csv "</li> <li>Available period: 2018 to 2021</li> <li>All sampling sites were public sites</li> <li>Names of active agents are in Italian</li> <li>Residue concentartions are given in "mg kg-1"</li> <li>"n.d." means "not detected"</li> <li>Lower limit of quantification (LOQ) was by default 0.01 mg kg-1</li> <li>GPS coordinates of the sampling sites are can be found in thefile "Coordinates_sampling_sites.csv"</li> </ul>

opencc-by-4.0Apr 2023View details →

MEaSUREs blue band total column water vapor sample data for the Ozone Monitoring Instrument

This dataset contains the MEaSUREs OMI Total Column Water Vapor (TCWV) data and their related data used in the paper titled “Development of the MEaSUREs blue band water vapor algorithm – Towards a long-term data record” by Wang et al. (2023). The unzipped archive contains the following three directories.  <ol> <li>OMI-H2O-L2/ contains the MEaSUREs Level 2 data (in molecules/cm2) in netCDF4 format for January and July 2005 and 2006. Selected supporting data are also included in each file.</li> <li>OMI-H2O-L3/ contains MRaSUREs Level 3 data (0.25 degree by 0.25 degree, in molecules/cm2) generated using the standard filtering criteria in netCDF4 format for January and July 2005 and 2006. Selected supporting data are also included.</li> <li>Model3_ncresult/ contains netCDF4 formatted files for the MEaSUREs OMI TCWV data (in mm), the AMSR_E TCWV data sampled onto the corresponding OMI pixel locations, and the LightGBM model 3 predictions for the OMI pixels.</li> </ol> The linux command ‘ncdump -h filename’ can be used to examine the contents of netCDF4 files. Due to the current size limit of Zenodo, only a small subset of the MEaSUREs data is archived here. The full dataset will be released elsewhere, e.g., NASA EARTHDATA GES DISC.

Data from: Non-invasive Assessment of Cartilage Damage of the Human Knee using Acoustic Emission Monitoring: a Pilot Cadaver Study

This dataset accompanies the following article: "Non-invasive Assessment of Cartilage Damage of the Human Knee using Acoustic Emission Monitoring: a Pilot Cadaver Study," in IEEE Transactions on Biomedical Engineering, doi: 10.1109/TBME.2023.3263388. Knee acoustic emissions (AE) recorded in the 100-450 kHz and 15-200kHz frequency ranges from a cadaver specimen knee in flexion/extension. Four stages of artificially inflicted cartilage damage and two sensor positions were investigated.  Stages of artificially inflicted cartilage damage: the cartilage surface damage on the medial compartment, KL III; the cartilage surface damage on the medial compartment plus patellofemoral surface, KL III; the cartilage surface damage on the medial compartment plus on the patellofemoral surface KL IV; the cartilage surface damage on the medial compartment plus on the patellofemoral surface and lateral compartment. Sensor positions: medial and lateral  knee

Spineless and overlooked: DNA metabarcoding of autonomous reef monitoring structures reveals intra- and interspecific genetic diversity in Mediterranean invertebrates

Sequence data and stepwise pipeline outputs associated with the article "Spineless and overlooked: DNA metabarcoding of autonomous reef monitoring structures reveals intra- and interspecific genetic diversity in Mediterranean invertebrates". Preprint available here: <a href="https://doi.org/10.22541/au.167085544.47638352/v1">10.22541/au.167085544.47638352/v1</a> Sequence data is deposited in fastq-format in folders by region (Palinuro.tar.gz, Livorno.tar.gz, and Rovinj.tar.gz) and a separate folder for controls (Controls.tar.gz). Each fastq-file contains sequences for a single PCR replicate named by sample and replicate number. Sample names are described in spineless_sample_names.csv. Positive control sequences are described in SM1_positive_controls.csv. Stepwise pipeline outputs are available in the folder Pipeline_outputs_stepwise.zip Scripts used to generate pipeline outputs as well as other aspects of the final article are available at <a href="https://github.com/thomasdotter/spineless-haplotypes">https://github.com/thomasdotter/spineless-haplotypes</a>.

opencc-by-4.0May 2022View details →

Digital Elevation Models, orthoimages and lava outlines of the 2021 Fagradalsfjall eruption: Results from near real-time photogrammetric monitoring

This repository contains the data behind the work described in Pedersen et al (in review), specifically the Digital Elevation Models (DEMs), orthoimages and lava outlines created as part of the near-real time monitoring of the Fagradalsfjall 2021 eruption (SW-Iceland). The processing of the data is explained in detail in the Supplement S2 of Pedersen et al (2022). The data derived from Pléiades surveys includes only the DEMs and the lava outlines. The Pléiades-based orthoimages are subject to license. Please contact the authors for further information about this. Convention for file naming: Data: DEM, Ortho, Outline YYYYMMDD_HHMM: Date of acquisition Platform used: Helicopter (HEL), Pléiades (PLE), Hasselblad A6D (A6D) Origin of elevations in DEMs: meters above ellipsoid (zmae) Ground Sampling Distance: 2x2m (DEM) and 30x30cm (Ortho) Cartographic projection: isn93 (see cartographic specifications for further details)   Cartographic specifications: Cartographic projection: ISN93/Lambert 1993 (EPSG: 3057, https://epsg.io/3057) Horizontal and vertical reference frame: The surveys after 18 April 2021 are in ISN2016/ISH2004, updated locally around the study area in April 2021 (after pre-eruptive deformations occurred). The rest of the surveys of late March and early April were created using several floating reference systems (see Supplement S3 for details), since no ground surveys were available during the first weeks of the data collection. The surveys of 23 March 2021, 31 March 2021 were re-procesed in Gouhier et al., 2022, using the survey done on 18 May 2021 as reference. Origin of elevations: Ellipsoid WGS84 Raster data format: GeoTIFF Raster compression system: ZSTD (http://facebook.github.io/zstd/) Vector data format: GeoPackage (https://www.geopackage.org/)

opencc-zeroMar 2023View details →

US National Native Bee Monitoring RCN Data Management Workshop: Public Domain Videos

The US National Native Bee Monitoring Research Coordination Network (RCN) held a two-day workshop on data management best practices for native bee inventory, survey, and monitoring data on March 28 and 30, 2023. Videos in this data set were played at the workshop. These videos are released into the public domain. This data set includes the following videos: <ul> <li>Ecological Metadata Standards to Enable Data Reuse by Julien Brun</li> <li>Useful Photo Management for Bee Species by Sam Droege</li> <li>Trait Data Models and Vocabulary by Jen Hammock</li> <li>Symbiota: open-source community portals for insect data management by Andrew Johnston</li> <li>Moving data from the field to the world by Jonathan Koch</li> <li>Exploring data using Discover Life by Clare Maffei</li> <li>USDA Data Sharing Policies and Opportunities by Cynthia Sims Parr</li> <li>Why Share Species Interaction Data? by Jorrit H. Poelen</li> <li>Big-Bee: Sharing Bee Interactions & Traits by Katja C. Seltmann</li> <li>Let’s talk about data by Katja C. Seltmann</li> <li>Responsible use of museum specimens & their data by Erika M. Tucker</li> </ul>

US National Native Bee Monitoring RCN Data Management Workshop: CC BY Videos

The US National Native Bee Monitoring Research Coordination Network (RCN) held a two-day workshop on data management best practices for native bee inventory, survey, and monitoring data on March 28 and 30, 2023. Videos in this data set were played at the workshop. These videos are released with a CC BY license. Please cite the presenter(s) of the video(s) you use. This data set includes the following videos: <ul> <li>The ABeeCs of Data Attribution: Please use your magic words by David Bloom</li> <li>Darwin Core Geography: How to make your locality data complete and accurate by David Bloom</li> <li>Best Practices for Managing Native Bee Molecular Data by Michael G. Branstetter</li> <li>A Trait Database for Bees by Elizabeth A. Crisfield</li> <li>Biotic interaction data and invasive species assessment by Quentin Groom</li> <li>OpenTraits Network (OTN) & TRY Plant Trait Database by Jens Kattge</li> <li>Semantics modeling of phenotypic trait data with ontologies by Diego S. Porto</li> <li>WorldFAIR: towards making plant-pollinator data FAIR by Maarten Trekels</li> </ul>

opencc-by-4.0May 2023View details →

Data from: Trends in butterfly populations in UK gardens – new evidence from citizen science monitoring

This data package describes the annual abundance indices and trend estimates for 22 butterfly species in UK gardens for the period 2007-2020. These data form the basis of the results presented in: Plummer, K.E., Dadam, D., Brereton, T., Dennis, E.B., Massimino, D., Risely, K. et al. (2023) Trends in butterfly populations in UK gardens—New evidence from citizen science monitoring. Insect Conservation and Diversity, 1– 13. Available from: <a href="https://doi.org/10.1111/icad.12645">https://doi.org/10.1111/icad.12645</a> Please refer to the paper for an explanation of the underlying BTO Garden BirdWatch (GBW) data and modelling protocols used to produce the datasets included here. We would also greatly appreciate if you could fill out <a href="https://forms.gle/DCc58VXpdmqnTmTk8" target="_blank" rel="noopener">this very short form</a> to tell us how you intend to use these data. Thanks in advance!

opencc-by-4.0Dec 2022View details →

Dataset: all TROPOMI detected plumes for 2021. [Schuit et al. 2023: Automated detection and monitoring of methane super-emitters using satellite data]

Dataset of all TROPOMI detected methane plumes in 2021, including estimates for the source location, emission quantification and source type. Corresponds to Figure 6 of Schuit et al. 2023 [Automated detection and monitoring of methane super-emitters using satellite data, https://doi.org/10.5194/acp-23-9071-2023]. Additional details and context are provided in Section 3 of the paper.   Contents and data formats date, date of the TROPOMI observation. format: YYYYMMDD time_UTC, time of the TROPOMI observation in UTC. format: HH:MM:SS lat, latitude of the center of the TROPOMI pixel at the estimated source location. format: float lon, longitude of the center of the TROPOMI pixel at the estimated source location. format: float source_rate_t/h, estimated emission source rate in tonnes per hour, the methodology is described in Section 2.5.1 of the paper. format: int uncertainty_t/h, the uncertainty of the emission source rate in tonnes per hour, the methodology is described in Section 2.5.1 of the paper. format: int estimated_source_type, the locally dominant anthropogenic source sector based on bottom-up inventories, the methodology is described in Section 2.5.3 of the paper. format: str   Full citation of the paper: Schuit, B. J., Maasakkers, J. D., Bijl, P., Mahapatra, G., van den Berg, A.-W., Pandey, S., Lorente, A., Borsdorff, T., Houweling, S., Varon, D. J., McKeever, J., Jervis, D., Girard, M., Irakulis-Loitxate, I., Gorroño, J., Guanter, L.,  Cusworth, D. H., and Aben, I.: Automated detection and monitoring of methane super-emitters using satellite data, Atmos. Chem. Phys., 23, 9071–9098, https://doi.org/10.5194/acp-23-9071-2023, 2023.

Dataset for 'Printed ecoresorbable temperature sensors for environmental monitoring'

This data set contains the data collected during the FNS project Green Piezo (Grant no. 179064) in association with the recent publication entitled “Printed ecoresorbable temperature sensors for environmental monitoring”. This work aims to study the effect of photonic sintering parameters on the temperature behavior of printed zinc resistors, with the aim to fabricate eco-friendly and ecoresorbable temperature sensors on paper. Biodegradable electronic devices have potential in tackling the increasingly pressing challenge of electronic waste and printing methods allow to reduce toxic byproducts of fabrication and wasted material. The sintering method that is optimized here is based on our previous work combining electrochemical and photonic sintering approaches to enable the fabrication of highly-conductive degradable metal tracks. We optimize the sintering parameters to obtain zinc resistors with a high temperature coefficient of resistance and a linear temperature response curve. The data that was collected in the frame of this work is present in this repository. More information about the contents of the dataset is present in the included README files.

Björkö Wind Turbine Version 1 (45kW) high frequency Structural Health Monitoring (SHM) data

The Chalmers wind turbine has variable speed operation with a direct driven generator and a frequency converter, it also has a digital control system developed by Chalmers. The wind turbine has a rated power of 45 kW and rated speed of 75 rpm. The wooden tower is 30 m high, the blades of carbon fibres are 7.5 m long, and the turbine diameter is 15.9 m. The individually blade pitch system is electrical. The turbine is situated on the island Björkö at Skarviksvägen, 20 km west of Göteborg city. The coordinates are: 57.71818820625921, 11.683382148764485. 69 SCADA and structural vibration and loads Channels timeseries (sampled at 20 and 100 Hz) such as nacelle accelerations, tower and blades bending moments are included. Structured metadata about wind turbine characteristics, SCADA, vibration and loads channels are included as JSON files and CSV. This particular dataset consisting of high frequency sampled data, is intended for condition and structural health analysis. The data covers: <ul> <li>the measurements sampled at 100 Hz correspond to the period from 05 July 2022 to 9 June 2023</li> <li>the measurements sampled at 20 Hz correspond to the period from 05 July 2022 to 2 August 2023</li> </ul> This repository includes: Time-series data in csv format: <ul> <li>B1_CL4_20.csv (this is the data sampled at 20 Hz)</li> <li>B1_CL4_100.csv (this is the data sampled at 100 Hz)</li> </ul> Metadata: <ul> <li>Bjorko_Sensors_Specs_Metadata.csv (Sensors signals specification in csv format)</li> <li>Bjorko_modes_mapping.csv (numerical integer value representing the wind turbine controller system mode in csv format)</li> <li>Bjorko_modes_mapping.json (numerical integer value representing the wind turbine controller system mode in csv JSON format)</li> <li>Bjorko_digital_io_states_mappings.csv (Description of digital input and output states in the wind turbine controller system in csv format)</li> </ul> Media: <ul> <li>Chalmers-Wind turbine.pdf (description of the wind turbine including pictures)</li> <li>Chalmers wind turbine description 220121-short.pdf (description of the wind turbine including pictures)</li> </ul> Semantic artifacts: <ul> <li>N/A</li> </ul> Other: <ul> <li>N/A</li> </ul> Additional information is available upon request.

Aventa AV-7 ETH Zurich Research Wind Turbine SCADA and high frequency Structural Health Monitoring (SHM) data

General description of wind turbine: The ETH owned wind turbine is Aventa AV-7, manufactured by Aventa AG in Switzerland and was commissioned in December 2002. The turbine is operated via a belt-driven generator and a frequency converter with a variable speed drive. The rated power of the Aventa AV-7 is 7 kW, beginning production at a wind speed of 2 m/s and having a cut-off speed of 14 m/s. The rotor diameter is 12.8 m with 3 rotor blades, and a hub height is 18m. The maximum rotational speed of the turbine is 63 rpm. The tower is a tubular steel-reinforced concrete structure, supported on concrete foundation, while the blades are made of glassfiber with a tubular steel main-spar. The turbine is regulated via a variable-speed and variable pitch control system. Location of site: The wind turbine is located in Taggenberg, about 5 km from the city centre of Winterthur, Switzerland. This site is easily accessible by public transport and on foot with direct road access right next to the turbine. This prime location reduces the cost of site visits and allows for frequent personal monitoring of the site when test equipment is installed. The coordinates of the site are: 47°31'12.2"N 8°40'55.7"E. Control and measurement systems and signals: The turbine is regulated via a variable-speed and collective variable pitch control system. SHM Motivation: Designed and commissioned in 2002, the Aventa wind turbine in Winterthur is soon reaching its end of design lifetime. In order to assess the various techniques of predicting the remaining useful lifetime, a Structural Health Monitoring (SHM) campaign was implemented by ETH Zurich. The monitoring campaign started in 2020, and is still ongoing. In addition, the setup is used as a research platform on topics such as system identification, operational modal analysis, faults/damage detection and classification. We analyze the influence of operational and environmental conditions on the modal parameters and to further infer Performance Indicators (PIs) for assessing structural behavior in terms of deterioration processes. Data Description: The tower and nacelle have been instrumented with 11 accelerometers distributed along the length of the tower, nacelle main frame, main bearing and generator. Two full bridge strain gauges are installed on the concrete tower based measuring fore-aft and side-side strain (and can be converted to bending moments) – all acceleration and strain signals sampled at 200Hz. Temperature and humidity are measured at the tower base – 1Hz data. In additional we are collecting operational performance data (SCADA), namely: wind speed, nacelle yaw orientation, rotor RPM, power output and turbine status – SCADA signals are sampled at 10Hz. See appendix for further details of the sensors layout. The measurements/instrumentation setup, type and layout is provided in the pdf files. The data: the data is provided in zip files corresponding to four use-cases as follows: <ul> <li>Normal operation data for system identification</li> <li>Aerodynamic imbalance on one blade</li> <li>Rotor icing event</li> <li>Failure of the flexible coupling of the linear drive of the collective pitch system</li> </ul> The data for each of the four uses-cases is organized in zip files. The content of each zip file is as follows: <ul> <li>Time-series data in HDF5 format</li> <li>Metadata: <ul> <li>Turbine specification (Aventa-AV-7.json and Aventa-AV-7.yaml)</li> <li>Sensor specification (Aventa_sensors.json )</li> <li>Unstructured description of the Aventa Turbine and the installed sensors (Aventa_Sensors_Specs.xlsx)</li> </ul> </li> <li>Semantic artifacts: <ul> <li>WindIO Wind Turbine YAML schema describing turbine specifications (IEAontology_schema.yaml)</li> <li>Sensor specification JSON schema (sensors_schema.json)</li> </ul> </li> <li>Media: Pictures of leading edge roughness and a clip of wind turbine operation</li> <li>Code: Jupyter notebook containing example code to load metadata from JSON and data from HDF5 files (example.ipynb)</li> </ul> Additional data is available upon request, please contact: <ul> <li>Prof. Dr. Eleni Chatzi (chatzi@ibk.baug.ethz.ch)</li> <li>Dr. Imad Abdallah (ai@rtdt.ai , abdallah@ibk.baug.ethz.ch)</li> </ul> For further details or questions, please contact: Prof. Dr. Eleni Chatzi Chair of Structural Mechanics & Monitoring ETH Zürich <a href="http://www.chatzi.ibk.ethz.ch/">http://www.chatzi.ibk.ethz.ch/</a>

Covid-19 Vaccine Monitoring project (CVM)-Electronic Health Record data sources Codelist

This is the code list that was used to identify outcomes and covariates (those tagged as in narrow) in electronic health records of participating data sources in the the CVM study which was addressing the following questions   1) To create and assess readiness of electronic health record data sources for rapid evaluation of safety signals by  <ul> <li> Providing an overview of the methods for identification of COVID-19 vaccine exposure in the data sources  </li> <li> Monitoring the number of individuals exposed to any COVID-19 vaccine and to compare this to COVID-19 vaccine exposure (benchmark: ECDC vaccine tracker)1   </li> <li> Generation of updated background rates for AESIs  </li> </ul> 2) To conduct rapid safety assessment studies using electronic healthcare records and support EMA safety assessments.   The protocol for this study is publicly available www.encepp.eu/encepp/viewResource.htm?id=42637. The report with results using the code list is publicly available on Zenodo as well.

opencc-by-4.0Jul 2023View details →

Processed metabolomic data from the EXPOsOMICS Personal Exposure Monitoring study

Metabolomic data from the 'Variability of the Human Serum Metabolome over 3 Months in the EXPOsOMICS Personal Exposure Monitoring Study' paper <a href="https://doi.org/10.1021/acs.est.3c03233">DOI: 10.1021/acs.est.3c03233</a> .  The data was originally collected and generated by the multicenter EXPOsOMICS Personal Exposure Monitoring study. Details on data collection and processing are described in the aforementioned paper. The statistical analysis from that paper is available at <a href="https://github.com/moosterwegel/variability-metabolites-paper">https://github.com/moosterwegel/variability-metabolites-paper</a> and may contain useful information/code to work with this data. `processed_covariate_data.csv`: ``` Rows: 298 Columns: 7 $ subjectid: hashed identifier subject $ sample_code: indicates if it's the first (A) or second (B) blood sample $ centre: indicates in which centre the data was collected $ age_cat: indicates age category at the time of a PEM session $ sq_sex:  indicates the sex of the participant (male, female) as filled in during the screening questionaire $ traf: indicates the exposure to traffic (PM2.5 and UFP) as measured during the PEM sessions.  $ bmi_cat: indicates BMI category at the time of a PEM session ``` `processed_lcms_data data.csv` contains the processed LCMS data: ``` Rows: 298 Columns: 4297 $ subjectid: hashed identifier subject $ sample_code: indicates if it's the first (A) or second (B) blood sample $ centre: indicates in which centre the data was collected $ compounds: measured features (compounds) are prefixed by the letter X. The name contains information on the measured monoisotopicmass_retentiontime. Non-detects (below limit of detection (LOD) are coded as 1 for the compounds. .... ``` In the datasets each row indicates a measurement on a day (`sample_code`) and person (`subjectid`). The datasets can be joined on these variables. The other data files (`annotations.xslx`, `ancestors_annotations.xlsx`, `annotations_plus_kegg_pathways.csv`) contain the annotations, ancestors of the annotations (to assign a class to a compound based on ChEBI ontology, see our paper for details), annotations plus KEGG pathways respectively.

Datasets supporting the publication "Technical Note: in-situ measurements and modelling of the oxidation kinetics in films of a cooking aerosol proxy using a Quartz Crystal Microbalance with Dissipation monitoring (QCM-D)" by Milsom et al.

Supporting experimental and modelling data for the manuscript entitled "Technical Note: Modelling and in-situ measurements of the oxidation kinetics in films of a cooking aerosol proxy using a Quartz Crystal Microbalance with Dissipation monitoring (QCM-D)" by Adam Milsom et al. 2023.  Includes raw QCM-D data with the numbers at the beginning of the files corresponding to the experiment numbers in the manuscript.  Normalised Raman peak area data for modelling and model ensemble outputs, including uptake coefficients.