Find research datasets worth reusing

opencc-by-4.0Feb 2019View details →

Real measurement of Coulomb diamonds

Data used in the paper "Efficiently measuring a quantum device using machine learning". https://arxiv.org/abs/1810.10042 It can be loaded directly from Numpy: import numpy as np data = np.load('data_real.npy')

opencc-by-4.0May 2018View details →

Data set for "Measuring synchronization and anticipation between individual investors from their daily performance"

The data stored here is used as a support of the paper "Measuring synchronization and anticipation between individual investors from their daily performance" where a measure based on Mutual Information and Transfer of Entropy is used in order to map investors' behaviour and which ones are following same behavioural patterns. The study linked to this data is published on pre-print Arxiv.org  with the following citation:     Mario Gutiérrez-Roig, Javier Borge-Holthoeffer, Alex Arenas and     Josep Perelló. Measuring synchronization and anticipation between     individual investors from their daily performance (2018)

Snow properties measurements (in situ & retrived from satellite) at Dome C, East Antarctica Plateau

The dataset contains the data inputs for the electromagnetic model: <ul> <li>the snow density profile down to 20 m depth (measured in 2010)</li> <li>the snow SSA profile down to 20 m depth (measured in 2010)</li> <li>the snow temperature profile down to 20 m depth (measured from 1 December 2006 to 4 October 2011)</li> </ul> The dataset contains also the data retrieved from satellite: <ul> <li>the retrieved surface snow density from AMSR-E satellite (obtained from 18 June 2002 to 4 October 2011)</li> </ul> The dataset contains finally the data measured in situ to compare with the data retrieved from satellite: <ul> <li>the surface snow density from CALVA program (measured from 3 February 2010 to 4 October 2011)</li> <li>the surface snow density from PNRA program measured in snow pits (measured from 18 December 2007 to 4 October 2011)</li> <li>the surface snow density from PNRA program measured next to stakes (measured from 9 May 2008 to 4 October 2011)</li> </ul>

Large scale experiments for an alternative erosion control measure using sand-filled geosystems. Data set produced at the CIEM flume, Hydralab+

Sand-filled geosystems have the potential to mimic aspects of natural and nature-based features that can enhance the resilience of coastal areas challenged by climate, with additional (structural) reinforcement. Knowledge gaps can be identified. For instance, (i) the sediment transport mechanisms around the geosystem; (ii) the amount of erosion in the leeside when the system is overtopped; (iii) quantitative contribution the geosystem for the wave overtopping reduction; and (iv) failure mechanisms of the geosystem under extreme conditions. Specific tests are proposed in order to fill the defined knowledge gap and answer the following research questions: <ol> <li>How do nearshore coastal processes (wave transformation, sediment transport) and wave structure interactions during extreme events differ from those during more usual big storm conditions for situations with and without the geosystem?</li> <li>How do feedbacks between the hydrodynamics and morphology of natural and nature-based features affect flooding, erosion, and recovery of coastal areas when erosion is limited by the 'geosystem'?</li> <li>How to conceive a dynamic coastal protection that can easily adapt to climate change in areas experiencing coastal squeeze (i.e. dense urban environment and human infrastructure with sea encroaching land) and vulnerable to coastal erosion and flooding risks?</li> </ol> The set of experiments, done at the large wave Flume (CIEM) in Barcelona, are here described in order to answer the previous questions. These experiments started on October 2018 and ended at the end of November 2018. These tests include different configurations: an initial benchmark tests in order to test the wave conditions were no geosystem protection is used, a second layout with a geotube used as a geosystem protection and finally a third layout were geobags are used as a protection.

Large scale experiments for an alternative erosion control measure using sand-filled geosystems. Data set produced at the CIEM flume, Hydralab+

Sand-filled geosystems have the potential to mimic aspects of natural and nature-based features that can enhance the resilience of coastal areas challenged by climate, with additional (structural) reinforcement. Knowledge gaps can be identified. For instance, (i) the sediment transport mechanisms around the geosystem; (ii) the amount of erosion in the leeside when the system is overtopped; (iii) quantitative contribution the geosystem for the wave overtopping reduction; and (iv) failure mechanisms of the geosystem under extreme conditions. Specific tests are proposed in order to fill the defined knowledge gap and answer the following research questions: <ol> <li>How do nearshore coastal processes (wave transformation, sediment transport) and wave structure interactions during extreme events differ from those during more usual big storm conditions for situations with and without the geosystem?</li> <li>How do feedbacks between the hydrodynamics and morphology of natural and nature-based features affect flooding, erosion, and recovery of coastal areas when erosion is limited by the 'geosystem'?</li> <li>How to conceive a dynamic coastal protection that can easily adapt to climate change in areas experiencing coastal squeeze (i.e. dense urban environment and human infrastructure with sea encroaching land) and vulnerable to coastal erosion and flooding risks?</li> </ol> The set of experiments, done at the large wave Flume (CIEM) in Barcelona, are here described in order to answer the previous questions. These experiments started on October 2018 and ended at the end of November 2018. These tests include different configurations: an initial benchmark tests in order to test the wave conditions were no geosystem protection is used, a second layout with a geotube used as a geosystem protection and finally a third layout were geobags are used as a protection.

Measurements of Pn velocity and anisotropy in Northwest Pacific region

Measurements of Pn velocity and anisotropy in Northwest Pacific region. The eight numbers in each line are the longitude, latitude, Pn velocity, velocity error, magnitude of Pn anisotropy, magnitude error, direction of the fastest wave propagation, and direction error at each grid.

opencc-by-4.0Dec 2018View details →

Code and data archive for Nettle et al. 'Consequences of measurement error in qPCR telomere data: A simulation study'

Code and data for Nettle et al. 'Consequences of measurement error in qPCR telomere data: A simulation study' Updated version of March 2019 Main simulation functions are contained in the script ‘simulation.functions.r’. When called, these functions (listed below) return datasets with requested properties containing both the ideal values of the quantities (Cqs, TS, etc.), and their post-error measured values. This allows the user to determine the differences between ideal and measured values, and perform other analyses. All simulation parameter values are user-specifiable. The script ‘paper.results.r’ reproduces all the figures and simulation results from the main paper. 'paper.results.r' also reads in the two .csv files of empirical data (dataset1 and dataset2). Datasets consist of observations from n individuals. The steps common to all of the simulation functions are as follows: <ul> <li>A vector of n true single copy gene abundances, true.dna.scg is defined, drawn from a normal distribution with mean b and standard deviation var.sample.size (b is a constant).</li> <li>A vector of n relative telomere lengths, true.telo.var is defined, drawn from a normal distribution with mean 1 and standard deviation telomere.var.</li> <li>Hence, the true abundance of the telomere sequence is defined, as a*true.dna.scg*true.telo.var. Here, a is a scaling constant representing how many copies of the telomeric sequence there are per single copy gene in the average sample.</li> <li>Ideal Cq values for both reactions are defined as f – log2(true.dna.scg) and f – log2(true.dna.telo), where f is a constant representing the chosen fluorescence threshold.</li> <li>Measurement errors in the Cqs are generated from a normal distribution with mean 0; standard deviations given by error.scg and error.telo; and a correlation between error.scg and error.telo given by error.cor.</li> <li>Hence, measured Cqs are generated, which can be compared to the ideal Cq values.</li> <li>TS ratios are calculated both on the measured Cqs, and the ideal ones.</li> </ul> The following functions are available. Specify desired parameter values in the parenthesis, e.g. generate.one.dataset(n=10000, error.telo=0.1, error.scg=0.1, error.cor=0). Default values in the simulation functions are generally those given in table 1 of the main paper. <ul> <li>generate.one.dataset() returns a simple dataset (one telomere measurement per individual) for chosen values of all the variables described in section 1. As well as ideal and measured Cqs, it returns ideal and measured TS ratios. It also returns the difference between the ideal and measured TS ratio, calculated two ways, computed (error.computed), and using equation (11) of online supplement 1 (error.analytic). Both methods produce the same number. This was included as an additional check of correctness of the simulation.</li> <li>generate.repeated.measure() returns a dataset where telomere lengths from the same individuals are measured twice, via two independent biological samples, and the true telomere length of each individual is assumed not to have changed at all. The data frame it returns is as for generate.one.dataset(), except that there are two of each variable (e.g. true.ts.1, true.ts.2, measured.ts.1, measured.ts.2, etc.).</li> <li>calculate.repeatability() calculates the repeatability of the measured T/S ratio (intra-class correlation coefficient) when generate.repeated.measure() is implemented using the given values for all the parameters. It requires prior installation of R package ‘irr’.</li> <li>compare.repeatability() returns the repeatability of the T/S ratio and the repeatability calculated on the raw Cq for the telomere reaction, for the given parameter values.</li> </ul>

opencc-by-4.0Apr 2019View details →

Fig. 10. Measuring the maximum width W in Micro-computed tomography for natural history specimens: a handbook of best practice protocols

Fig. 10. Measuring the maximum width W (in pixels) of the projected specimen (as the distance from the rotation axis - dotted line - to the farthest end of the sample) to calculate the number of radiographs needed. This measurement is done for the angular position of the rotating platform where the projected specimen is the widest. For a complete rotation, the projected specimen would stay within the limits of the rectangle. Photo by MNHN.

opencc-by-4.0May 2019View details →

PsPM-PIT2 : PSR, SCR, ECG and respiration measurements from pavlovian to instrumental transfer tasks with visual CS and electrical US.

This dataset consists of a three session experiment conducted with 38 healthy unmedicated participants (22 females and 16 males aged 24.8 +/- 3.6). The sessions were recorded on the same day with a self decided break. The first session is an instrumental conditioning, the second a classical (Pavlovian) discriminant delay fear conditioning and the third a Pavlovian-to-instrumental transfer task. For all three sessions this dataset contains electrocardyogramm (ECG), pupil size (PSR), respiration, and skin conductance (SCR) measurements. For the fear conditioning session CS+/CS- are visual stimuli of differently colored backgrounds. US is a train of electric square pulses delivered with a constant current stimulator on participants' dominant forearm through a pin-cathode/ring-anode configuration. SOA between the CS and US is 3.5 s. ITI is randomly determined on each trial to be an integer between 7 - 11 s. The instrumental stimuli are two images of differently colored vending machines cuing if the machine required a coin to dispense a chocolate (Approach) or if a coin should be averted from the machine, to prevent an already dispensed chocolate from being crushed by a newly dispensed soft drink can (Withdraw). The operant task asks subjects to repeatedly press the space key (Go) or withhold any key press (NoGo) to win chocolates. In the instrumental conditioning session, subjects play the game with grey background, and in the Pavlovian-to-instrumental transfer session they play the same operant game but with CS colored backgrounds instead of grey background.

opencc-by-4.0Apr 2019View details →

PsPM-PIT1 : PSR, SCR, ECG and respiration measurements from pavlovian to instrumental transfer tasks with visual CS and electrical US.

This dataset consists of a three session experiment conducted with 22 healthy unmedicated participants (13 females and 9 males aged 26.2 +/- 3.6). The sessions were recorded on the same day with a self decided break. The first session is an instrumental conditioning, the second a classical (Pavlovian) discriminant delay fear conditioning and the third a Pavlovian-to-instrumental transfer task. For all three sessions this dataset contains electrocardiogram (ECG), pupil size (PSR), respiration and skin conductance (SCR) measurements. For the fear conditioning session CS+/CS- are visual stimuli of differently colored backgrounds. US is a train of electric square pulses delivered with a constant current stimulator on participants' dominant forearm through a pin-cathode/ring-anode configuration. SOA between the CS and US is 3.0 s. ITI is 2.5 s. The instrumental stimuli are two images of differently colored vending machines cuing if the machine required a coin to dispense a chocolate (Approach) or if a coin should be averted from the machine, to prevent an already dispensed chocolate from being crushed by a newly dispensed soft drink can (Withdraw). The operant task asks subjects to repeatedly press the space key (Go) or withhold any key press (NoGo) to earn chocolates. In the instrumental conditioning session, subjects play the game with grey background, and in the Pavlovian-to-instrumental transfer session they play the same operant game but with CS colored backgrounds instead of grey background.

Supplementary data to Schmiester et al. Efficient parameterization of large-scale dynamic models based on relative measurements

This archive contains Supplementary data to the manuscript Efficient parameterization of large-scale dynamic models based on relative measurements by Leonard Schmiester, Yannik Schälte, Fabian Fröhlich, Jan Hasenauer and Daniel Weindl.

FT45 Stehle 12-key tenoroon: measurements, photos, endoscopic video

Dataset of FT45 Stehle 12-key tenoroon containing measurements, photos, and an endoscopic video.

FT47 Riva 13-key tenoroon: measurements, photos, endoscopic video

Dataset of FT47 Riva 13-key tenoroon containing detailed measurements, photos, and an endoscopic video.

FT43 Leiberz 7-key fagottino: measurements, photos, endoscopic video

Dataset of FT43 Leiberz 7-key fagottino containing measurements, photos, and an endoscopic video.

FT38 Savary Jeune (8) 12-key tenoroon: measurements, photos, endoscopic video

Dataset FT38 Savary Jeune (8) 12-key tenoroon containing detailed external and internal measurements, photos, and an endoscopic video.

opencc-by-4.0Feb 2019View details →

anatomical measurement of cervical lateral mass for cervical pedicle screw and paravertebral foramen screw insertion

The anatomical pedicle transverse angle (PTA) and linear parameters of the lateral mass were measured, and the relationship between the calculated angles and the anatomical PTA was investigated. θp was defined as the convergence angle from the posterolateral edge of the lateral mass to the pedicle, and θc was defined as the convergence angle from the posterolateral edge of the lateral mass to the anterolateral corner of the vertebral foramen. The thickness of the cortical bone of the medial wall of the lateral mass (cT) and the medial (mT) and lateral (lT) walls of the pedicle at C3–7 were also measured.

openbsd-3-clauseJul 2019View details →

Cold Ion Measurements Enabled by Electrostatic Instrument Biasing: Implementation and Modeling Results

Cold Ion Measurements Enabled by Electrostatic Instrument Biasing: Implementation and Modeling Results   This archive contains the data file for the five CPIC simulations run for paper Larsen et al 2019.  [1, 2]. The data files contents and format are described.   Each data file is stored in HDF5 format written with h5py [3, 4]. The files named field_potential_bias_XX_data.h5 contain the electric field components and electric potential with the following structure: <blockquote> + :|____Author (str [50]) :|____Bias (str [3]) :|____DOI (str [22]) :|____Date (str [14]) :|____Description (str [81]) :|____File_Creator (str [44]) :|____License (str [2140]) |____E (h5py._hl.dataset.Dataset (3, 101, 101, 101))     :|____Description (str [41])     :|____Format (str [94])     :|____Units (str [3]) |____XYZ (h5py._hl.dataset.Dataset (3, 101)) |____phi (h5py._hl.dataset.Dataset (101, 101, 101))   </blockquote> See the metadata within the file for units of each variable.   The files named detector_bias_XX_data.h5 contain the particles collected at the simulated detector in a mix of physical and CPIC units with the following structure: <blockquote> <pre>+</pre> <pre>:|____Author (str [50])</pre> <pre>:|____Bias (str [4])</pre> <pre>:|____DIO (str [22])</pre> <pre>:|____Date (str [14])</pre> <pre>:|____Description (str [81])</pre> <pre>:|____Detector_area (str [11])</pre> <pre>:|____File_Creator (str [44])</pre> <pre>:|____License (str [2140])</pre> <pre>:|____Timestep (str [12])</pre> <pre>:|____Timestep_description (str [114])</pre> <pre>:|____electron_temperature (str [6])</pre> <pre>:|____photoemission_electron_energy (str [4])</pre> <pre>:|____potential_at_boundary (str [3])</pre> <pre>:|____proton_electron_mass_ratio (str [4])</pre> <pre>:|____proton_temperature (str [7])</pre> <pre>:|____reference_density (str [9])</pre> <pre>|____Energy (h5py._hl.dataset.Dataset (2296,))</pre> <pre>    :|____Description (str [32])</pre> <pre>    :|____Units (str [2])</pre> <pre>|____Velocity (h5py._hl.dataset.Dataset (2296,))</pre> <pre>    :|____Description (str [59])</pre> <pre>    :|____Units (str [3])</pre> <pre>|____Velocity_CPIC (h5py._hl.dataset.Dataset (2296,))</pre> <pre>    :|____Description (str [59])</pre> <pre>    :|____Units (str [4])</pre> <pre>|____Vx (h5py._hl.dataset.Dataset (2296,))</pre> <pre>    :|____Description (str [44])</pre> <pre>    :|____Units (str [3])</pre> <pre>|____Vx_CPIC (h5py._hl.dataset.Dataset (2296,))</pre> <pre>    :|____Description (str [44])</pre> <pre>    :|____Units (str [4])</pre> <pre>|____Vy (h5py._hl.dataset.Dataset (2296,))</pre> <pre>    :|____Description (str [44])</pre> <pre>    :|____Units (str [3])</pre> <pre>|____Vy_CPIC (h5py._hl.dataset.Dataset (2296,))</pre> <pre>    :|____Description (str [44])</pre> <pre>    :|____Units (str [4])</pre> <pre>|____Vz (h5py._hl.dataset.Dataset (2296,))</pre> <pre>    :|____Description (str [44])</pre> <pre>    :|____Units (str [3])</pre> <pre>|____Vz_CPIC (h5py._hl.dataset.Dataset (2296,))</pre> <pre>    :|____Description (str [44])</pre> <pre>    :|____Units (str [4])</pre> <pre>|____phi (h5py._hl.dataset.Dataset (2296,))</pre> <pre>    :|____Description (str [41])</pre> <pre>    :|____Units (str [3])</pre> <pre>|____q (h5py._hl.dataset.Dataset (2296,))</pre> <pre>    :|____Description (str [47])</pre> <pre>    :|____Units (str [4])</pre> <pre>|____species (h5py._hl.dataset.Dataset (2296,))</pre> <pre>    :|____Description (str [26])</pre> <pre>|____theta (h5py._hl.dataset.Dataset (2296,))</pre> <pre>    :|____Description (str [37])</pre> <pre>    :|____Units (str [3])</pre> <pre>|____timestep (h5py._hl.dataset.Dataset (2296,))</pre> <pre>    :|____Description (str [41])</pre> <pre>|____weight (h5py._hl.dataset.Dataset (2296,))</pre> <pre>    :|____Description (str [38])</pre> <pre>    :|____Units (str [4])</pre> </blockquote>   The information in parentheses are the datatype and size, square brackets, [], denote a single element of that many characters and parentheses, (), denote the stored array size. Lines with a leading colon, :, are metadata fields to make the data files more usable.   1.         Delzanno, G.L., et al., CPIC: a curvilinear particle-in-cell code for plasma–material interaction studies. IEEE Transactions on Plasma Science, 2013. 41(12): p. 3577-3587. 2.         Meierbachtol, C.S., et al., An electrostatic Particle-In-Cell code on multi-block structured meshes. Journal of Computational Physics, 2017. 350: p. 796-823. 3.         Folk, M., A. Cheng, and K. Yates. HDF5: A file format and I/O library for high performance computing applications. in Proceedings of supercomputing. 1999. 4.         Collette, A., et al., h5py/h5py 2.9.0. 2018.

FT17 Jacoby Fils 5-key fagottino: measurements, photos, endoscopic video

Dataset of FT17 Jacoby Fils 5-key fagottino containing detailed external and internal measurements, photos, and an endoscopic video.

opencc-by-4.0Jul 2019View details →

FT21 Porthaux 9-key tenoroon: measurements, photos, endoscopic video

Dataset of FT21 Porthaux 9-key tenoroon containing detailed external and internal measurements, photos, and an endoscopic video.

FT15 Grenser & Wiesner 6-key fagottino: measurements, photos, endoscopic video

Dataset of FT15 Grenser & Wiesner 6-key fagottino containing detailed external and internal measurements, photos, and an endoscopic video.