Physical and biogeochemical oceanography data from Conductivity, Temperature, Depth (CTD) rosette deployments during the Antarctic Circumnavigation Expedition (ACE).
Contributors: Henry, Tahlia, Robinson, Charlotte, Haumann, F. Alexander, Thomas, Jenny, Hutchings, Jennifer, Schuback, Nina, Tsukernik, Maria, Leonard, Katherine
... ***** Dataset abstract ***** This data set contains measurements from various sensors mounted on the Conductivity, Temperature, Depth (CTD) rosette that was deployed in the Southern Ocean during the Antarctic Circumnavigation Expedition (ACE). 63 CTD casts were carried out during three legs in the period 21st December 2016 to 16th March 2017, including one test cast and one failed cast, for which no data is available. Data include temperature, salinity, pressure, dissolved oxygen, oxygen saturation, chlorophyll-a concentration, backscatter, and photosynthetically active radiation (PAR) and reported are also the computed variables density, depth, and sound velocity. All data has been quality controlled and post-cruise calibrated, except for the oxygen data. Data is provided at 1 dbar pressure intervals for the up- and down-casts separately and as a merged bottle file when Niskin bottles were closed. This circumpolar data set provides insights into the circumpolar hydrography and biogeochemistry of the Southern Ocean during one austral summer season. ***** Dataset contents ***** For transparency, the raw files and files produced at the intermediate stages of data processing have been provided, in addition to the final processed files. Raw data files: - ace_ctd_raw_files.zip - includes raw files direct from instrument and XMLCON configuration files Intermediate files: - files output at each stage of the SeaBird processing Processed data files: - ace_ctd_CTD20190805CURRSGCMR - one final set of files for the complete sensor data; - ACE_BOTTLE20190807CURRSGCM_hy1.csv - a merged bottle file extracted from the sensor data is also provided Metadata: - range of files describing the CTD deployments, sensors, water sampling, quality-checking and processing of the files. ***** Dataset license***** This physical and biogeochemical oceanography dataset is made available under the Open Data Commons Attribution License (ODC-By) v1.0 whose full text can be found at https://www.opendatacommons.org/licenses/by/1.0/index.html
Contributors: Ana Viñuela, Arushi Varshney, Martijn van de Bunt, Rashmi Prasad, Olof Asplund, Amanda Bennett, Michael Boehnke, Andrew Brown, Michael Erdos, João Fadista
... Summary statistics for the InsPIRE study: Influence of genetic variants on gene expression in human pancreatic islets – implications for type 2 diabetes This datasets includes eQTLs from 420 pancreatic islets (exon and gene level quantifications) and 27 beta-cells FAC sorted (exon quantifications) form RNA-Seq samples. Full summary statistics and independent associations are included. The full description of methods is currently available here: Bioxiv (https://www.biorxiv.org/content/10.1101/655670v1).
A neural network-based estimate of the seasonal variability of total alkalinity in the East China Sea shelf
... In order to estimate the seasonal variability of total alkalinity in the ECS shelf, an artificial neural network (ANN) model was developed using 5 cruise datasets from 2008 to 2018. The model used temperature, salinity, and dissolved oxygen to estimate AT with a root-mean-square error of ~7 umol kg-1, and was applied to fill missing alkalinity data for 8 cruises during 2013-2016. In addition, monthly water column AT for the period 2000-2016 was also obtained passing temperature, salinity, and dissolved oxygen from the Changjiang Biology Finite-Volume Coastal Ocean Model (FVCOM) Data. Spatial distributions, seasonal cycles and correlations of surface AT indicated that the seasonal fluctuation of the Changjiang River discharge is the major factor affecting seasonal variation of surface total alkalinity in the ECS shelf. The largest seasonal fluctuation of surface total alkalinity was found on the inner shelf near the Changjiang Estuary.
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Retrieving monthly and interannual pHT in the East China Sea shelf using an artificial neural network: ANN-pHT-v1
... The reliability of the artificial neural network model was evaluated by independent sampled data from 3 cruises in 2018. Monthly water column pHT for the period 2000-2016 was obtained passing T, S, DO, N, P, and Si from the Finite-Volume Coastal Ocean Model with the European Regional Sea Ecosystem Model through the artificial neural network. The spatiotemporal resolution of monthly pHT is 1-10 km in the horizontal, 10 depth levels in the vertical, and 12 months. Seasonal pHT dynamics in the East China Sea shelf can be primarily attributed to temperature changes and the shifting balance of production and respiration processes.
Contributors: Elkady, Ahmed, Lignos, Dimitirios G.
... IIDAP is a standalone MATLAB-based program that was first developed in 2018 at the Resilient Steel Structures Laboratory (RESSLAB) in Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland. With 10K+ lines of code IIDAP is capable of performing a variety of dynamic analysis procedures ranging from simple response history analysis to response spectrum and incremental dynamic analyses on single degree-of-freedom systems (SDoF). IIDAP includes a wide-ranging library of SDoF system models ranging from simple non-deteriorating linear and bilinear models to state-of-the-art deteriorating bilinear pinched and peak-oriented responses. A seismic hazard module is also included for the quantification of collapse risk. IIDAP is highly beneficial for both educational and research purposes.
Contributors: C Ferreira, Jailton
... The transformation of coordinates and time from an inertial frame to another inertial frame is obtained without using rigid measuring-rods and clocks as primitive entities. The obtained transformation is applied to some cases.
Model outputs: Historical (1700–2012) Global Multi-model Estimates of the Fire Emissions from the Fire Modeling Intercomparison Project (FireMIP)
Contributors: Li, Fang, Rabin, Sam S., Val Martin, Maria, Hantson, Stijn, Andreae, Meinrat O., Arneth, Almut, Lasslop, Gitta, Yue, Chao, Bachelet, Dominique, Forrest, Matthew
... This dataset contains the fire model outputs of emissions for 34 species (elements, compounds, and classes of compounds) as described in the following: Li, F., Val Martin, M., Hantson, S., Andreae, M. O., Arneth, A., Lasslop, G., Yue, C., Bachelet, D., Forrest, M., Kaiser, J. W., Kluzek, E., Liu, X., Melton, J. R., Ward, D. S., Darmenov, A., Hickler, T., Ichoku, C., Magi, B. I., Sitch, S., van der Werf, G. R., Wiedinmyer, C., and Rabin, S.: Historical (1700–2012) Global Multi-model Estimates of the Fire Emissions from the Fire Modeling Intercomparison Project (FireMIP), Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2019-37, accepted pending technical corrections, 2019. See Readme for more information.
Contributors: Piotr Domagalski, Lars Roar Sætran
... Herewith we present the extended 1Hz dataset of wind measurements from a Skipheia meteorological station on the island of Frøya on the western coast of Norway, Trondelag. The data binned in 10 min averages can be find at: https://doi.org/10.5281/zenodo.2557500 The site represents an exposed coastal wind climate with open sea, land and mixed fetch from various directions. UTM-coordinates of the Met-mast: 8.34251 E and 63.66638 N. See the map for details (NorwegianMapping Authority): https://www.norgeskart.no/#!?project=norgeskart&layers=1003&zoom=3&lat=7035885.49&lon=539601.41&markerLat=7077031.483032227&markerLon=170902.83203125&panel=searchOptionsPanel&sok=Titranveien Presented data were gathered between years 2009-2016. Data&hardware summary: Years 2009-2016: Mast2 equipped with 6 pairs of 2D sonic anemometers at 10, 16, 25, 40, 70, 100 m above the ground, independent temperature measurements at the same heights and near the ground; pressure and relative humidity from local meteostation (Sula, 20 km away). Years 2014-2016: Mast4 equipped with 2 pairs of 2D sonic anemometers at 40 and 100 m above the ground. The distance between the masts is 79 m. Data is binned in years and months and stored in a ‘*.txt’ tab-separated values file. Data column order is described in SkipheiaMast2_header.txt and SkipheiaMast4_header.txt, where WSx is the wind speed (m/s), WDx is the wind direction (360 deg), ATx is the air temperature (deg C) and x designates the instrument number. The instruments are numbered starting from the ground. Example: For Mast2 (6 pairs of anemometers, ground temperature + 6 temperature sensors on the mast) that means that AT0 is the ground temperature. WS1 and WS2 are wind speed records at 10 m level. WS3 and WS4 are wind speed records at 16 m. For Mast4 (2 pairs of anemometers) that means that WS1 and WS2 are wind speed records at 40 m level. WS3 and WS4 are wind speed records at 100 m. Detailed site description with wind climate description can be found in attached analysis: Site analysys.pdf. Additional information and analysis can be found in listed below works, using data from Frøya site: Bardal, L. M., & Sætran, L. R. (2016, September). Spatial correlation of atmospheric wind at scales relevant for large scale wind turbines. In Journal of Physics: Conference Series (Vol. 753, No. 3, p. 032033). IOP Publishing, doi:10.1088/1742-6596/753/3/032033, https://iopscience.iop.org/article/10.1088/1742-6596/753/3/032033/pdf Bardal, L. M., & Sætran, L. R. (2016). Wind gust factors in a coastal wind climate. Energy Procedia, 94, 417-424, https://doi.org/10.1016/j.egypro.2016.09.207 IEA Wind TCP Task 27 Compendium of IEA Wind TCP Task 27 Case Studies, Technical Report, Prepared by Ignacio Cruz Cruz, CIEMAT, Spain Trudy Forsyth, WAT, United States, October 2018; Chapter 1.8. https://community.ieawind.org/HigherLogic/System/DownloadDocumentFile.ashx?DocumentFileKey=8afc06ec-bb68-0be8-8481-6622e9e95ae7&forceDialog=0 Domagalski, P., Bardal, L. M., & Sætran, L. Vertical Wind Profiles in Non-neutral Conditions-Comparison of Models and Measurements from Froya. Journal of Offshore Mechanics and Arctic Engineering, doi: 10.1115/1.4041816, http://offshoremechanics.asmedigitalcollection.asme.org/article.aspx?articleid=2711333&resultClick=3 Mathias Møller , Piotr Domagalski and Lars Roar Sætran, Characteristics of abnormal vertical wind profiles at a coastal site, Journal of Physics: Conference Series, IOPscience, under review (Feb 2019), DeepWind2019 conference poster available at: https://www.sintef.no/globalassets/project/eera-deepwind-2019/posters/c_moller_a4.pdf
Contributors: Sigrist, Lukas, Gomez, Andres, Thiele, Lothar
... Dataset Information This dataset presents long-term term indoor solar harvesting traces and jointly monitored with the ambient conditions. The data is recorded at 6 indoor positions with diverse characteristics at our institute at ETH Zurich in Zurich, Switzerland. The data is collected with a measurement platform  consisting of a solar panel (AM-5412) connected to a bq25505 energy harvesting chip that stores the harvested energy in a virtual battery circuit. Two TSL45315 light sensors placed on opposite sides of the solar panel monitor the illuminance level and a BME280 sensor logs ambient conditions like temperature, humidity and air pressure. The dataset contains the measurement of the energy flow at the input and the output of the bq25505 harvesting circuit, as well as the illuminance, temperature, humidity and air pressure measurements of the ambient sensors. The following timestamped data columns are available in the raw measurement format, as well as preprocessed and filtered HDF5 datasets: V_in - Converter input/solar panel output voltage, in volt I_in - Converter input/solar panel output current, in ampere V_bat - Battery voltage (emulated through circuit), in volt I_bat - Net Battery current, in/out flowing current, in ampere Ev_left - Illuminance left of solar panel, in lux Ev_right - Illuminance left of solar panel, in lux P_amb - Ambient air pressure, in pascal RH_amb - Ambient relative humidity, unit-less between 0 and 1 T_amb - Ambient temperature, in centigrade Celsius The following publication presents and overview of the dataset and more details on the deployment used for data collection. A copy of the abstract is included in this dataset, see the file abstract.pdf. L. Sigrist, A. Gomez, and L. Thiele. "Dataset: Tracing Indoor Solar Harvesting." In Proceedings of the 2nd Workshop on Data Acquisition To Analysis (DATA '19), 2019. Folder Structure and Files processed/ - This folder holds the imported, merged and filtered datasets of the power and sensor measurements. The datasets are stored in HDF5 format and split by measurement position posXX and and power and ambient sensor measurements. The files belonging to this folder are contained in archives named yyyy_mm_processed.tar, where yyyy and mm represent the year and month the data was published. A separate file lists the exact content of each archive (see below). raw/ - This folder holds the raw measurement files recorded with the RocketLogger [1, 2] and using the measurement platform available at . The files belonging to this folder are contained in archives named yyyy_mm_raw.tar, where yyyy and mmrepresent the year and month the data was published. A separate file lists the exact content of each archive (see below). LICENSE - License information for the dataset. README.md - The README file containing this information. abstract.pdf - A copy of the above mentioned abstract submitted to the DATA '19 Workshop, introducing this dataset and the deployment used to collect it. raw_import.ipynb [open in nbviewer] - Jupyter Python notebook to import, merge, and filter the raw dataset from the raw/ folder. This is the exact code used to generate the processed dataset and store it in the HDF5 format in the processed/folder. raw_preview.ipynb [open in nbviewer] - This Jupyter Python notebook imports the raw dataset directly and plots a preview of the full power trace for all measurement positions. processing_python.ipynb [open in nbviewer] - Jupyter Python notebook demonstrating the import and use of the processed dataset in Python. Calculates column-wise statistics, includes more detailed power plots and the simple energy predictor performance comparison included in the abstract. processing_r.ipynb [open in nbviewer] - Jupyter R notebook demonstrating the import and use of the processed dataset in R. Calculates column-wise statistics and extracts and plots the energy harvesting conversion efficiency included in the abstract. Furthermore, the harvested power is analyzed as a function of the ambient light level. Dataset File Lists Processed Dataset Files The list of the processed datasets included in the yyyy_mm_processed.tar archive is provided in yyyy_mm_processed.files.md. The markdown formatted table lists the name of all files, their size in bytes, as well as the SHA-256 sums. Raw Dataset Files A list of the raw measurement files included in the yyyy_mm_raw.tar archive(s) is provided in yyyy_mm_raw.files.md. The markdown formatted table lists the name of all files, their size in bytes, as well as the SHA-256 sums. Dataset Revisions v1.0 (2019-08-03) Initial release. Includes the data collected from 2017-07-27 to 2019-08-01. The dataset archive files related to this revision are 2019_08_raw.tar and 2019_08_processed.tar. For position pos06, the measurements from 2018-01-06 00:00:00 to 2018-01-10 00:00:00 are filtered (data inconsistency in file indoor1_p27.rld). v1.1 (2019-09-09) Revision of the processed dataset v1.0 and addition of the final dataset abstract. Updated processing scripts reduce the timestamp drift in the processed dataset, the archive 2019_08_processed.tar has been replaced. For position pos06, the measurements from 2018-01-06 16:00:00 to 2018-01-10 00:00:00 are filtered (indoor1_p27.rld data inconsistency). Dataset Authors, Copyright and License Authors: Lukas Sigrist, Andres Gomez, and Lothar Thiele Contact: Lukas Sigrist (email@example.com) Copyright: (c) 2017-2019, ETH Zurich, Computer Engineering Group License: Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) References  L. Sigrist, A. Gomez, R. Lim, S. Lippuner, M. Leubin, and L. Thiele. Measurement and validation of energy harvesting IoT devices. In Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017.  ETH Zurich, Computer Engineering Group. RocketLogger Project Website, https://rocketlogger.ethz.ch/.  L. Sigrist. Solar Harvesting and Ambient Tracing Platform, 2019. https://gitlab.ethz.ch/tec/public/employees/sigristl/harvesting_tracing
Data from: Improved STEREO simulation with a new gamma ray spectrum of excited gadolinium isotopes using FIFRELIN
Contributors: Almazán Molina, Helena, Bernard, Laura, Blanchet, Adrien, Bonhomme, Aurélie, Buck, Christian, Chebboubi, Abdelaziz, del Amo Sánchez, Pablo, El Atmani, Ilham, Haser, Julia, Kandzia, Felix
... Supplemental material to the article “Improved STEREO simulation with a new gamma ray spectrum of excited gadolinium isotopes using FIFRELIN” The files available are aimed to simulate the de-excitation cascade following neutron capture on 155Gd and 157Gd. Therefore, the FIFRELIN simulation was done for the 156Gd and 158Gd isotopes, with the initial condition of an excitation energy of E* = Sn, the neutron separation energy. Please cite this publication when using the provided files: arXiv:1905.11967 [physics.ins-det]