Find research datasets worth reusing

opencc-by-4.0Sep 2021View details →

BLE RSS dataset for fingerprinting radio map calibration

The dataset contains Bluetooth Low Energy signal strengths measured in a fully furnished flat. The dataset was originally used in the study concerning RSS-fingerprinting based indoor positioning systems. The data were gathered using a hybrid BLE-UWB localization system, which was installed in the apartment and a mobile robotic platform equipped for a LiDAR. The dataset comprises power measurement results and LiDAR scans performed in 4104 points. The scans used for initial environment mapping and power levels registered in two test scenarios are also attached. The set contains both raw and preprocessed measurement data. The Python code for raw data loading is supplied. The detailed dataset description can be found in the dataset_description.pdf file. When using the dataset, please consider citing the original paper, in which the data were used: M. Kolakowski, “Automated Calibration of RSS Fingerprinting Based Systems Using a Mobile Robot and Machine Learning”, Sensors , vol. 21, 6270, Sep. 2021 <a href="https://doi.org/10.3390/s21186270">https://doi.org/10.3390/s21186270</a>

opencc-by-4.0Nov 2022View details →

Dataset for the article EDM-GNSS distance comparison at the EURO5000 calibration baseline: preliminary results

GNSS RINEX observation files for the observation campaign used in the article "EDM-GNSS distance comparison at the EURO5000 calibration baseline: preliminary results" by Kinga Wezka, Luis García-Asenjo, Dominik Próchniewicz, Sergio Baselga, Ryszard Szpunar, Pascual Garrigues, Janusz Walo and Raquel Luján, Journal of Applied Geodesy https://doi.org/10.1515/jag-2022-0049. The work leading to this paper was performed within the 18SIB01 GeoMetre project of the European Metrology Programme for Innovation and Research (EMPIR). This project has received funding from the EMPIR programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme, funder ID: 10.13039/100014132. Raquel Luján acknowledges the funding from the Programa de Ayudas de Investigación y Desarrollo (PAID-01-20) de la Universitat Politècnica de València.

opencc-by-sa-4.0Sep 2022View details →

Dataset for the calibration of the photosynthetic unit (PSU)-model developed at KIT

The presented dataset was used to develop and calibrate (estimate the model parameters) a photosynthetic unit (PSU)-model. The PSU-model and the data were generated at the Institute of Process Engineering in Life Sciences – Bioprocess Engineering of the KIT under the supervision of Prof. Dr.-Ing. Clemens Posten. Please use this data to familiarize yourself with the PSU-model and as an example dataset. If you want to use your own data in this context, organize it like the sample data listed here. As an alternative, you should adapt the model scripts to your data accordingly. Date of data collection: The data were generated as a part of the author’s PhD program between 2018-2022 at Karlsruhe Institute of Technology (KIT).

opencc-by-4.0Apr 2023View details →

Video demonstrating dynamic calibration procedure

Video recording shows dynamic calibration procedure, developed in the frameworks of ComTraForce project. The calibration was performed at the facilities of RISE Research Institutes of Sweden.

opencc-by-4.0Apr 2023View details →

Video demonstrating continuous calibration procedure

Video recording shows continuous calibration procedure, developed in the frameworks of ComTraForce project. Authors would like to acknowledge SincoTec Test Systems GmbH for providing calibration facilities and explicitly Head of Measurement Department Mike Udhe for providing technical support.

opencc-by-4.0Apr 2023View details →

Fast Method for Calibrated Self-Discharge Measurement of Lithium-Ion Batteries including Temperature Effects and Comparison to Modelling

Self-discharge data related to the manuscript entitled: 'Fast Method for Calibrated Self-Discharge Measurement of Lithium-Ion Batteries including Temperature Effects and Comparison to Modelling', submitted to Energy Reports on 26 April 2023.

opencc-by-4.0May 2023View details →

METRIC - Multi-Eye To Robot Indoor Calibration Dataset

The METRIC dataset comprises more than 10,000 synthetic and real images of ChAruCo and checkerboard patterns. Each pattern is securely attached to the robot's end-effector, which is systematically moved in front of four cameras surrounding the manipulator. This movement allows for image acquisition from various viewpoints. The real images in the dataset encompass multiple sets of images captured by three distinct types of sensor networks: Microsoft Kinect V2, Intel RealSense Depth D455, and Intel RealSense Lidar L515. The purpose of including these images is to evaluate the advantages and disadvantages of each sensor network for calibration purposes. Additionally, to accurately assess the impact of the distance between the camera and robot on calibration, we obtained a comprehensive synthetic dataset. This dataset contains associated ground truth data and is divided into three different camera network setups, corresponding to three levels of calibration difficulty based on the cell size.

opencc-by-4.0Aug 2023View details →

Dataset for: Improving the accuracy of δ18O and δ17O values of O2 measured by continuous-flow isotope-ratio mass spectrometry with a multi-point isotope-ratio calibration

Dataset containing raw and treated isotope-ratio mass spectrometry and laser absorption spectroscopy data, accompanying the manuscript "Improving the accuracy of δ18O and δ17O values of O2 measured by continuous-flow isotope-ratio mass spectrometry with a multi-point isotope-ratio calibration". This dataset also contains O2 concentration and partial pressure data used for figures in the associated manuscript.

edi44/100

Parramore Island of the Virginia Coast Reserve Permanent Plot Resurvey: Calibration Saltmarsh Stem data 1996

First 3-5 year resurvey of permanent monitoring plots using essentially the same protocol as the intial survey of 1992-1993 except that: 1.) standing biomass of the herbaceous groundcover was added (including for new lower salt marsh plots) using clip plots at the subplot locations; and 2.) an estimate of landcover/habitat class aggregation was conducted surrounding each plot center out to 60m in the four cardinal directions. Extends baseline data useful for estimating landscape-scale vegetative productivity, mortality, and turnover; for establishing pre-disturbance conditions in the case of later stand- or island-wide disturbance; and for assisting in the ground-truthing of landcover and habitat classification using aerial or satellite remote-sensing imagry.

openCustomJan 1999View details →

edi44/100

Parramore Island of the Virginia Coast Reserve Permanent Plot Resurvey: Calibration Saltmarsh Harvest data 1996

openCustomJan 1999View details →

edi44/100

Parramore Island of the Virginia Coast Reserve Permanent Plot Resurvey: Calibration Saltmarsh Plot data 1996

openCustomJan 1999View details →

opencc-by-4.0Dec 2019View details →

Data related to the manuscript "Bayesian Calibration and Validation of a Large-scale and Time-demanding Sediment Transport Model"

1) Riverbed_Elevation_Measurements.txt     Description: Measured riverbed geometry of available years     Columns: Node ID, Easting [m], Northig [m], Elevation 2002 [m asl], Elevation 2005 [m asl], Elevation 2010 [m asl], Elevation        2013 [m asl] ---------------------------------------------------------------------------------------------------------------------------- 2) Hydro_FT_2D_manual.txt     Description: Simulation results of the manually calibrated full model     Columns: Node ID, Easting [m], Northig [m], Elevation 2005 [m asl], Elevation 2010 [m asl], Elevation 2013 [m asl] 3.1) Hydro_FT_2D_CollocationPointBase.txt     Description: Parameter combinations of the collocation point base for each of the 20 simulations conducted with the full model to      construct the surrogate     Rows: Critical Shields parameter, Grain Roughness, Grain Size distribution 3.2) Hydro_FT_2D_CollocationResults.txt     Description: Simulation results of the 20 simulations conducted with the full model at the collocation points     Columns: Node ID, Easting [m], Northig [m], Elevations 2005 [m asl] of simulation 1 through 20, Node ID, Easting [m asl], Northig     [m asl], Elevations 2010 [m asl] of simulation 1 through 20, Node ID, Easting [m asl], Northig [m asl], Elevations 2013 [m asl] of     simulation 1 through 20 ---------------------------------------------------------------------------------------------------------------------------- 4.1) aPC_MC_N_Combinations_Weights_prior.txt     Description: ID of prior MC runs with tested parameter combinations and corresponding importance weights     Rows: ID of MC runs, Critical Shields parameter, Grain Roughness, Grain Size distribution, importance weights 4.2) aPC_MC_2005_prior.txt     Description: aPC surrogate results of prior MC runs for 2005     Columns: Node ID, Easting [m], Northig [m], Elevations 2005 [m asl] of MC run 1 through 100,000 4.3) aPC_MC_2010_prior.txt     Description: aPC surrogate results of prior MC runs for 2010     Columns: Node ID, Easting [m], Northig [m], Elevations 2010 [m asl] of MC run 1 through 100,000 4.4) aPC_MC_2013_prior.txt     Description: aPC surrogate results of prior MC runs for 2013     Columns: Node ID, Easting [m], Northig [m], Elevations 2013 [m asl] of MC run 1 through 100,000      4.5) aPC_MC_N_Combinations_Weights_posterior.txt     Description: ID of accepted (posterior) MC runs with tested parameter combinations and corresponding importance weights     Rows: ID of accepted MC runs, Critical Shields parameter, Grain Roughness, Grain Size distribution, importance weights 4.6) aPC_MC_2005_posterior.txt     Description: aPC surrogate results of posterior MC runs for 2005     Columns: Node ID, Easting [m], Northig [m], Elevations 2005 [m asl] of accepted MC run 1 through 857 4.7) aPC_MC_2010_posterior.txt     Description: aPC surrogate results of posterior MC runs for 2010     Columns: Node ID, Easting [m], Northig [m], Elevations 2010 [m asl] of accepted MC run 1 through 857 4.8) aPC_MC_2013_posterior.txt     Description: aPC surrogate results of posterior MC runs for 2013     Columns: Node ID, Easting [m], Northig [m], Elevation 2013 [m asl] of accepted MC run 1 through 857 ---------------------------------------------------------------------------------------------------------------------------- 5) aPC_MAP.txt     Description: Simulation results conducted with the stochastically calibrated aPC surrogate model using the MAP parameter      combination     Columns: Node ID, Easting [m], Northig [m], Elevation 2005 [m asl], Elevation 2010 [m asl], Elevation 2013 [m asl] 6) Hydro_FT_2D_MAP.txt     Description: Simulation results conducted with the stochastically calibrated full model using the MAP parameter combination     Columns: Node ID, Easting [m], Northig [m], Elevation 2005 [m asl], Elevation 2010 [m asl], Elevation 2013 [m asl] ---------------------------------------------------------------------------------------------------------------------------- 7) dz.txt     Description: Riverbed Evolution for all nodes in the section of interest (n=1138) obtained with differently calibrated models for all         considered time periods     Columns: Node ID, Easting [m asl], Northig [m asl], aPC_prior 2005 [m], aPC_posterior 2005 [m], aPC_MAP 2005 [m],      Hydro_FT-2D_MAP 2005 [m], Hydro_FT-2D_manual 2005 [m], aPC_prior 2010 [m], aPC_posterior 2010 [m], aPC_MAP 2010 [m],     Hydro_FT-2D_MAP 2010 [m], Hydro_FT-2D_manual 2010 [m], aPC_prior 2013 [m], aPC_posterior 2013 [m], aPC_MAP 2013 [m],     Hydro_FT-2D_MAP 2013 [m], Hydro_FT-2D_manual 2013 [m] 8) dz_CalibrationNodes.txt     Description: Riverbed Evolution for calibration nodes (n=204) obtained with differently calibrated models for all considered time     periods     Columns: Node ID, Easting [m asl], Northig [m asl], aPC_prior 2005 [m], aPC_posterior 2005 [m], aPC_MAP 2005 [m],      Hydro_FT-2D_MAP 2005 [m], Hydro_FT-2D_manual 2005 [m], aPC_prior 2010 [m], aPC_posterior 2010 [m], aPC_MAP 2010 [m],     Hydro_FT-2D_MAP 2010 [m], Hydro_FT-2D_manual 2010 [m], aPC_prior 2013 [m], aPC_posterior 2013 [m], aPC_MAP 2013 [m],     Hydro_FT-2D_MAP 2013 [m], Hydro_FT-2D_manual 2013 [m]

FIG. 8 in A review of fossil Bursidae and their use for phylogeny calibration

FIG. 8. — Stratigraphic range of the extinct and extant Bursidae. Black arrows indicate total stratigraphic range; grey arrows indicate regional stratigraphic range. Abbreviations: P, Peru; WI, Western Indian Ocean; AB, Aquitaine Basin; CWA, Caribbean, Western Atlantic Ocean; PT, Paratethys, CWT: Central Western Tethys; EA, Eastern Atlantic; CWM, Central Western Mediterranean; WP, Western Pacific Ocean; J, Java, Indo-Pacific; S, Sumatra, Indo-Pacific; EP, Eastern Pacific Ocean. Grey squares regroup accepted genera and putative genera within Bursa.

FIG. 5 in A review of fossil Bursidae and their use for phylogeny calibration

FIG. 5. — Extinct Bursidae species: A, B, Marsupina judensis Beu, 2010; NMB H 18308, holotype, late Miocene, Punta Judas, Pacific Costa Rica, reproduced from Beu (2010); C, D, "Bursa" landaui Harzhauser, 2009, NHMW 2007z0181/0029 (C) holotype and NHMW 2007z0181/0030 (D), both from the Aquitanian of Ras Tipuli, Lindi Bay, Tanzania, reproduced from Harzhauser (2009); E, Lampadopsis sangirana (Beu, 2005), RGM 456 230, holotype, late Pliocene of Kalibeng layers, Sangiran, central Java, reproduced from Beu (2005); F, G, Aquitanobursa chipolana (Schmelz, 1997) n. comb., USNM 647108, paratype, specimen illustrated by Vokes (1973: text-figs 2a, b), coral reef facies of Chipola Formation (late Early Miocene), loc. TU547, reproduced from Beu (2010); H, I, Marsupina freya (Olsson, 1932), PRI 2312, holotype, "Zorritos Miocene," divide between Quebrada Conchudo Bravo and Quebrada Seca,Mancora, Peru; J, K, Olssonia chira (Olsson, 1930) n. comb., PRI 24257, holotype, Late Eocene/Oligocene of Chira Formation, Quercotilla, Peru; L-O, Olssonia yasila (Olsson, 1930) n. comb., PRI 24254 (L, M) and PRI 24255 (N, O), Middle Eocene Talara Formation, Yasila, Peru; P-S, Aquitanobursa morrisi (d'Archiac & Haime, 1853) n. comb., original representation of one of the syntypes by d'Archiac & Haime (1853) (P, R) and (NHMUK PI TG 27045) lectotype,from "Calcaire grossier jaune de la chaîne d'Hala" (Aquitanian?) Pakistan; T, Aquitanobursa amphitrites (Maury, 1917) n. comb., PRI 28763, holotype, Cercado Fm (Late Miocene), Maury's bluff 3, Cercado de Mao, Dominican Republic, reproduced from Beu (2010). Scale bar: 2 cm.

FIG. 1 in A review of fossil Bursidae and their use for phylogeny calibration

FIG. 1. — Taxa excluded from Bursidae: A, B, Hanaibursa aquilana (Parona, 1909) UMUT MM15655, Hiraiga Formation (Aptian of Japan), reproduced from Kase (1984: pl. 28, fig. 16a); A, dorsal view; B, ventral view; C, D, Bursa saundersi Adegoke, 1977, UIMG 394, holotype, Ewekoro Formation (Selandian of Nigeria), reproduced from Adegoke (1977: pl. 31, figs 27, 28); C, ventral view; D, dorsal view. Scale bar: 2 cm.

FIG. 3 in A review of fossil Bursidae and their use for phylogeny calibration

FIG. 3. — Recent Bursidae: A, IM-2008-5350, Bursa condita (Gmelin,1791), « Nouvelle Calédonie », Jousseaume coll.; B, IM-2007-43072, Bufonaria perelegans Beu, 1987, Santo Marine Biodiversity Survey 2006, Vanuatu; C, IM-2009-11906, Bursa corrugata (Perry, 1811), Dakar'09, Senegal; D, IM-2007-43063, Tutufa rubeta (Linnaeus, 1758), Santo Marine Biodiversity Survey 2006, Vanuatu; E, IM-2007-40338, Bursa latitudo Garrard, 1961, Exploration de la Biodiversité et Isolement en mer du Corail, Nouvelle Calédonie; F, IM-2009-5150, Lampadopsis rhodostoma (G.B. Sowerby II, 1835), MAINBAZA, Mozambique channel; G, IM-2009-5148, Bursa granularis (Röding, 1798), juvenile, MAINBAZA, Mozambique channel; H, IM-2009-11653, Bursa quirihorai Beu, 1987, Exploration de la Biodiversité et Isolement en mer du Corail, New Caledonia. All are ventral views. Scale bars: A-D, 2 cm; E-H, 1 cm.

openother-openDec 2019View details →

A revised list of fossil calibrations to constrain molecular dating of angiosperms

Original list of calibrations used in: Ramírez-Barahona S, Sauquet H, Magallón S. 2020. The delayed and geographically heterogenous diversification of flowering plant families. Nature Ecology & Evolution: 10.1038/s41559-020-1241–3.

opencc-by-4.0May 2020View details →

Bayesian evaluation of the mass calibration example from EA 4/02

The example describes how the measurement uncertainty of the calibration of a 10 kg weight can be performed using Bayes’ rule and a measurement model. The purpose of the example is to demonstrate how Bayes’ rule can be implemented in the context of the Guide to the expression of Uncertainty in Measurement and the uncertainty can be propagated. Special attention is paid to the assignment of probability density functions of type A and type B evaluations of standard uncertainty. The files contained in the dataset are: <ul> <li>Readme.txt: instructions on how to install and run the project</li> <li>Mass_example_EA_4_02.pdf: Report "Bayesian evaluation of the mass calibration example from EA 4/02"</li> <li>Compendium.bib: bibliography file</li> <li>Mass_example_EA_4_02_Article.Rproj: RStudio project file</li> <li>Mass_example_EA_4_02.Rnw: source file to generate Mass_example_EA_4_02.tex in RStudio</li> <li>Mass_example_EA_4_02.bbl: file generated when compiling Mass_example_EA_4_02.tex</li> <li>Mass_example_EA_4_02.log: file generated when compiling Mass_example_EA_4_02.tex</li> <li>Mass_example_EA_4_02.synctex.gz: file generated when compiling Mass_example_EA_4_02.tex</li> <li>Mass_example_EA_4_02.tex: LaTeX file to be compiled in order to produce Mass_example_EA_4_02.pdf</li> <li>Mass_example_EA_4_02-concordance.tex: file generated by RStudio when compiling Mass_example_EA_4_02.Rnw.</li> </ul>

opencc-by-4.0Dec 2020View details →

Unraveling a black box: An open-source methodology for the field calibration of small air quality sensors

This repository contains data for the manuscript: "Unraveling a black box: An open-source methodology for the field calibration of small air quality sensors."   This includes: Raw data from the low-cost prototype EarthSense Zephyrs, as well as raw data from reference instrumentation. SC stands for "Summer Campaign" and WC stands for "Winter Campaign", denoting the two different campaigns assessed in this study.   Abstract The last two decades have seen substantial technological advances in the development of low-cost air pollution instruments using small sensors. While their use continues to spread across the field of atmospheric chemistry, challenges remain in ensuring data quality and comparability of calibration methods. This study introduces a seven-step methodology for the field calibration of low-cost sensors using reference instrumentation with user-friendly guidelines, open access code, and a discussion of common barriers to such an approach. The methodology has been developed and is applicable for gas-phase pollutants, such as for the measurement of nitrogen dioxide (NO2) or ozone (O3). A full example of the application of this methodology to a case study in an urban environment using both Multiple Linear Regression (MLR) and the Random Forest (RF) machine-learning technique is presented with relevant R code provided, including error estimation. In this case, we have applied it to the calibration of metal oxide gas-phase sensors (MOS). Results reiterate previous findings that MLR and RF are similarly accurate, though with differing limitations. The methodology presented here goes a step further than most studies by including explicit, transparent steps for addressing model selection, validation, and tuning, as well as addressing the common issues of autocorrelation and multicollinearity. We also highlight the need for standardized reporting of methods for data cleaning and flagging, model selection and tuning, and model metrics. In the absence of a standardized methodology for the calibration of low-cost sensors, we suggest a number of best practices for future studies using low-cost sensors to ensure greater comparability of research.