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zenodo44/100

Experimental measurements of creep deformation of Tournemire shale loaded at specified pressure (10 MPa) and room temperature (26°C)

Following the experimental protocol used in (Geng et al., 2018), we performed the stepping creep experiments at a confining pressure of 10 MPa. We first loaded the samples under hydrostatic conditions up to 10 MPa at a pressure rate of 0.3 MPa/min. Hydrostatic conditions were maintained for ~18 h at 26 °C. Next, differential stress (axial stress minus confining pressure) was increased to a fixed initial stress (30 MPa) and maintained (creep status) for 24 h. The differential stress was repeatedly increased by 5 MPa and maintained for 24 h, until brittle failure. All the experiments were conducted using the triaxial apparatus installed at the Laboratoire de Géologie of ENS-Paris (France). There were few constraints on the natural saturation state of the samples because of their low permeability (10-19 10-21 m2). To avoid exposition redundancy, an additional description of the technical performance of the triaxial apparatus can be referred to (Brantut et al., 2011, Sarout & Guéguen, 2008). Compressive stresses and compactive strains are denoted as positive. Axial creep deformation was measured using three capacitive gap sensors that externally monitored the overall axial displacement of the piston during creep deformation. Volumetric strain during creep was estimated by adding the average of axial strains (axial displacement of the piston divided by the sample length) and two average radial strains measured by four radial strain gauges glued uniformly around the cylindrical rock surface. As the deformation rate generally stabilized during the last 8 h in most creep periods (Geng et al., 2018), we estimated the average axial strain rate over the last 8 h of each step to characterize the creep strain rate under the corresponding axial loading stress. More technical details of the sample configuration and creep rates estimation can be found in (Geng et al., 2018).

opencc-by-4.0Jan 2021View details →

zenodo44/100

Data set with length measurments of Machu Picchu

The base of a niche was considered to be a construction level where in the past the architectural module could be applied. To check this possibility the width and the distances between niches were measured in the 3D point cloud for further cosine quantogram analysis. Thus, 11 data sets were created, each corresponding to a particular sector or distinguish part of it, from the area so-called zona urbana in Machu Picchu site. The size of each sample depends on the amount and size of buildings in a sector, so samples vary from 44 to 244 measurements, measured in centimetre [cm].

opencc-by-4.0Jan 2021View details →

zenodo44/100

A Free Database of Head-Related Impulse Response Measurements in the Horizontal Plane with Multiple Distances (MAT-Version)

opencc-by-4.0Jan 2021View details →

zenodo44/100

Shock Ramp Compressions Measurements of Iron on the Sandia National Laboratories' Z-Machine

This data contains 1) the apparent velocity data from Velocity Interferometer System for Any Reflector (VISAR) data analyzed using the PointVISAR program for experiments Z3155 and Z3339 and 2) the equation of state results from analyzing the velocity data using a backward integration -- forward Lagrangian analysis. These experiments were performed on the Sandia National Laboratories' Z-Machine, where the iron samples were dynamically compressed via shocked compression to approximately 275 Gpa and further ramp compression to approximately 400 GPa. This covers pressure-temperature regions near the melt line as well as the interior conditions of terrestrial planets. The Z3155 data include four samples, each with two VISAR traces, and the Z3339 data include six samples, each with two or three VISAR traces. The apparent velocity can be corrected to true velocity using the latest lithium fluoride window correction for a 532 nm wavelength. PointVISAR is available as part of the Sandia Matlab AnalysiS Hierarchy (SMASH) toolbox. Details of the backward integration -- forward Lagrangian anaylsis that was used can be found in the related publication. Example data file interpretation: "Z3155_north_panel_bot_sample_01.txt" is the first VISAR trace from the bottom sample of the north panel on experiment Z3155. "Z3155_EoS_combined.txt" is the sample-averaged Equation of State result from experiment Z3155. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. SAND2020-13961 O

opencc-by-4.0Jan 2021View details →

zenodo44/100

FT5 Schubert 13-key tenoroon: measurements, photos, endoscopic video

Dataset of FT5 Schubert 13-key tenoroon containing detailed external and internal measurements, photos, and an endoscopic video (formerly listed as "Anonymous 6").

opencc-by-4.0Apr 2019View details →

zenodo44/100

Optimizing a Cantilever Measurement System towards High Speed, Nonreactive Contact-Resonance-Profilometry (Data)

Raw data, scripts and figures used for the article "Optimizing a Cantilever Measurement System towards High Speed, Nonreactive Contact-Resonance-Profilometry", published in Proceedings on 21 Nov 2018. The data/scripts can be opened/executed by the software "Matlab"

opencc-by-4.0Feb 2021View details →

zenodo44/100

Galaxy Zoo DECaLS: Detailed Visual Morphology Measurements from Volunteers and Deep Learning for 314,000 Galaxies

This repository contains the data released in the paper "Galaxy Zoo DECaLS: Detailed Visual Morphology Measurements from Volunteers and Deep Learning for 314,000 Galaxies" (DOI to follow on publication). We release detailed morphology catalogues, both volunteer and automated, for Galaxy Zoo DECaLS. - gz_decals_volunteers_1_and_2 contains volunteer classifications for galaxies classified during the GZD-1 and GZD-2 campaigns. - gz_decals_volunteers_5 similarly contains classifications from the GZD-5 campaign. Note that GZD-5 used a modified schema designed to better detect mergers and weak bars, and includes many galaxies with only approx. five volunteer responses. - gz_decals_auto_posteriors contains the predicted posteriors for volunteer responses to all galaxies used in any campaign. The full posteriors are recorded as Dirichlet distribution concentrations. gz_decals_auto_posteriors also summarises these posteriors as the automated equivalent of previous Galaxy Zoo data releases; the expected vote fractions (mean posteriors). Note that not all posteriors/vote fractions are relevant for every galaxy; we suggest assessing relevance using the estimated fraction of volunteers that would have been asked each question. We include a schema document, schema.md, to define the column names in each catalogue. We also release the galaxy images shown to volunteers on www.galaxyzoo.org during GZD-5. The images on which the automated classifier was trained may be derived from these volunteer-facing images. These images are split into four zip files, each of which contains images named by iauname inside a subfolder named by the first four characters in their iauname. Not all images were labelled during GZD-5 - refer to the catalog for training labels. We are working with the Zenodo team to add these large files to this repository - meanwhile, you can download them from The University of Manchester <a href="https://docs.google.com/document/d/1YgpnxiSJ7ffOW6FY8pX0pw93LTu8rLIdPL2PYhxW1fo/edit?usp=sharing">here</a>. The .csv and .parquet files contain identical data. Parquet is a fast column-oriented binary format which can be read with pd.read_parquet(loc, columns=[some columns]). You may also be interested in the <a href="https://github.com/mwalmsley/zoobot">github repository</a> which contains code to reproduce the model and to fine-tune it for new tasks (including pretrained weights). We will release updates if needed via Zenodo versioning. We recommend using the latest version of this repository. You can check the version you are currently viewing on the right-hand sidebar. Please cite the paper (DOI to follow on publication) when using the data in this repository. --- History v0.0.1 (submission) provides the catalog files. v0.0.2 (first revision) renames the catalog files, adds flags for poorly sized galaxies, and includes the galaxy images via the University of Manchester

opencc-by-4.0Dec 2020View details →

zenodo44/100

Oscillations of Offshore Wind Turbines undergoing Installation I: Raw Measurements

Overview This repository contains data from an offshore measurement campaign conducted during the installation of the offshore wind farm trianel wind farm borkum II (https://www.trianel-borkumzwei.de/). The wind farm consists of 32 Senvion 6XM152 turbines. The installation took place between August 2019 and May 2020. An offshore wind turbine undergoing installation is interesting from a research point of view for several reasons: <ul> <li>Simple geometry: turbine foundation and tower are both rotationally symmetric steel tubes. Rotational symmetry also leads to (approximate) isotropical structural characteristics in the plane normal to tower and foundation.</li> <li>High Reynolds number flow: Assuming a tower diameter of 6 m, and average wind speeds ranging from 5 m/s to 12 m/s under installation conditions, Reynolds numbers range from 4.5 million to 10.5 million.</li> <li>Wave loading under full-scale conditions.</li> <li>Practical relevance to improving the competetivity of offshore wind.</li> </ul> For fluid mechanics, closely monitoring offshore wind turbines under wind and wave loading thus compares to a full-scale experiment. Monitoring 32 turbines undergoing installation thus enables the measurement a broad spectrum of different states. The investigation into the data is ongoing, questions and contributions are welcome. The current data release still does not include all data. The dataset will thus be updated again in the future with more data to come. First analytic results can be found here: Sander, A, Haselsteiner, AF, Barat, K, Janssen, M, Oelker, S, Ohlendorf, J, & Thoben, K. "Relative Motion During Single Blade Installation: Measurements From the North Sea." Proceedings of the ASME 2020 39th International Conference on Ocean, Offshore and Arctic Engineering. Volume 9: Ocean Renewable Energy. Virtual, Online. August 3–7, 2020. V009T09A069. ASME. <https://doi.org/10.1115/OMAE2020-18935> Sander, A, Meinhardt, C & Thoben, KD. "Monitoring of Offshore Wind Turbines under Wind and Wave Loading during Installation" Proceedings of the EuroDyn 2020 XI International Conference on Structural Dynamics. Volume 1. Virtual, Online. November 23-26, 2020. <https://generalconferencefiles.s3-eu-west-1.amazonaws.com/eurodyn_2020_ebook_procedings_vol1.pdf> Recordings of the conference presentations are available on youtube: <ul> <li>OMAE20: https://www.youtube.com/watch?v=QcAwdv6Z4e4</li> <li>EURODYN20: https://www.youtube.com/watch?v=iL-jAe0luTw</li> </ul> Physical Background <ol> <li>An offshore wind turbine under installation conditions can be simplified as a cantilevered beam (circular cross-section, rotationally symmetric wall thickness) with an eccentric mass (nacelle with generator) vibrating transversally (fore-aft and side-side in the reference system of the nacelle) under wind and wave loads.</li> <li>Both wind and wave loads are stochastic and are described using statistical models.</li> <li>Wave loads are a function of the sea state. For a sea state, the most important parameters are significant wave heigh H_m0 and Wave peak period T_P. To a lesser extend, wave direction, zero upcrossing period and maximum wave height are also important. Different statistical models can be used to describe the sea state and the relationship between significant wave heigh H_m0 and wave peak period. Most prominent in the North Sea is the JONSWAP spectrum.</li> <li>Wind loads are depending on wind speed, wind direction, shear factor and turbulence intensity. Different statistical models are available to describe the wind spectrum.</li> <li>Wind and wave loads trigger a structural response of the turbine. The structural response depends on the loading spectrum as well as the transfer function. Furthermore, the structural response is strongly depending on the damping and elasticity of the structure. In turbines, damping is typically very low (~ 0.5 - 1.5 %).</li> <li>The response is dominated by the first Eigenfrequency of the turbine. As the turbine is assumed to be rotationally symmetric, the fore-aft and side-side mode are extremely close together if not indistinguishable [1].</li> <li>The response has the characteristics of a narrow-band random vibration. A narrow-band random vibration is characterized by being dominated by a single, narrow frequency peak (here: first Eigenfrequency). The amplitude envelope follows a Rayleigh distribution and the phase angle is equally distributed between 0 and 2 pi.</li> <li>If viewed from above, the structural response describes a closed curve (orbit) which can be characterized by it shape (eccentricity), mean amplitude and direction. Mathematically speaking, this is a lissajous-figure, where the time series from one response direction is plotted as a function of the time series of the second response direction.</li> </ol> [1]: under installation condition Experimental Setup Several locations were used to record data during the installation of the wind farm. They are listed in the following table: <ul> <li>helihoist-{1,2}: data recorded from the helicopter hoisting platform atop the turbine nacelle. For most installations, two sensor boxes were deployed to ensure data availability.</li> <li>tp: Measurements from the transition piece</li> <li>sbitroot: Measurements from the blade lifting yoke's blade root side. The Z-axis is aligned to the blade main axis, X-Axis is perpendicular.</li> <li>sbittip: Measurements from the tip side of the blade lifting yoke. Z-axis aligned with the blade main axis, X-axis perpendicular</li> <li>damper: Measurements from the tuned mass damper used during single blade installation</li> <li>towertop: measurements from inside the turbine tower at the upper lift plattform</li> <li>towertransfer: measurements from atop the towers during sail out from the base harbour to the installation site </li> </ul> Organization of data For each turbine installation, a separate folder can be found, e.g. turbine-01 for the first and turbine-16 for the 16th turbine. Turbine numbering follows the order of installation. Different data sources are organized in subfolders for each turbine dataset. Unfortunately, not every data source is available for each turbine. Data sources are roughly sorted into categories. The following table lists these categories: <ul> <li>location / tom : data from custom build sensor boxes. Data includes acceleration, angular acceleration, magnetic field, gnss recording and rough estimates of the eulerian angles.</li> <li>waves / wmb-sued : Sea state statistics for the installatin period of the turbine.</li> <li>waves / fino : Sea state statistics from the german research platform FINO1 located approx. 6 km from the installation site.</li> <li>waves / waveradar : Sea state statstics, recorded by a wave rider wave laser. </li> <li>wind / lidar : high fidelity wind data recorded on the installation vessel during the installation of the wind farm.</li> <li>wind / scada : 10 min. mean wind statistisc recorded on wind turbines in the vicinity of the installation site. This data is used in case no LIDAR data is available.</li> <li>wind / anemometer : During some of the installations, anemometers were present on the installation vessel. These recordings are sorted into this sub-subfolder.</li> <li>wind / fino : Additonal wind statistics recorded by the FINO research station. Least recommended for investigations, as these recordings were taken approx. 6 km from the installation site.</li> </ul> The zenodo data set includes 16 zip archives (for 16 turbines) as well as one zip archive including environmental data. The following lists the folder structure of the turbine-04.zip archive (with most of the data files removed for clarity). <pre><code class="language-bash">└── turbines ├── turbine-04 │ ├── helihoist-1 │ │ └── tom │ │ └── clean │ │ ├── turbine-04_helihoist-1_tom_clean_2019-09-01-11-27-17_2019-09-01-11-54-00.csv │ │ ├── turbine-04_helihoist-1_tom_clean_2019-09-01-11-54-00_2019-09-01-12-20-44.csv │ ├── sbitroot │ │ └── tom │ │ └── clean │ │ ├── turbine-04_sbitroot_tom_clean_2019-09-07-06-48-53_2019-09-07-07-17-16.csv │ │ ├── turbine-04_sbitroot_tom_clean_2019-09-07-07-17-16_2019-09-07-07-45-59.csv │ ├── towertop │ │ └── tom │ │ └── clean │ │ ├── turbine-04_towertop_tom_clean_2000-01-06-18-55-52_2000-01-06-20-30-10.csv │ │ ├── turbine-04_towertop_tom_clean_2000-01-06-20-30-11_2000-01-06-22-04-31.csv │ ├── towertransfer │ │ └── tom │ │ └── clean │ │ ├── turbine-04_towertransfer_tom_clean_2019-08-31-03-11-53_2019-08-31-04-00-09.csv │ │ ├── turbine-04_towertransfer_tom_clean_2019-08-31-04-00-10_2019-08-31-04-48-19.csv │ └── tp │ └── tom │ └── clean │ ├── turbine-04_tp_tom_clean_2019-08-31-18-34-45_2019-08-31-19-07-52.csv │ ├── turbine-04_tp_tom_clean_2019-08-31-19-08-00_2019-08-31-19-40-43.csv </code></pre> The following list the contents of the environment.zip archive. Note that again most of the data files have been removed for clarity.  <pre><code class="language-bash">└── environment ├── waves │ └── wmb-sued │ ├── wmb-sued_2019-08-15.csv │ ├── wmb-sued_2019-08-16.csv │ ├── wmb-sued_2019-08-17.csv └── wind └── lidar ├── lidar_2019-08-03.csv ├── lidar_2019-08-04.csv ├── lidar_2019-08-05.csv </code></pre> TOM data description The abbreviation TOM referes to Tower Oscillation Measurement and the data that was acquired using a specific set of sensor boxes built by university of Bremen for this specific purpose. These Sensor Boxes were initially designed to measure accelerations and GPS tracks of offshore wind turbine towers undergoing installation. During the installation of the wind farm Trianel Windpark Borkum II they were subsequentially used to track the complete installation with a focus on single blade installation Data from the TOM devices comes as CSV files. The firmware of the boxes was designed, such that data was written into 10 MB sized txt files instead of one large txt file in order to circumvent data corruption due to power loss. However, this leads to a few milliseconds of missing data between log files. Additionally, jitter due to IO-Operations is present in the data as well and data should under all circumstance be resampled befor further analysis. Parameters provided by the TOM devices are listed in the following: <ul> <li>epoch : machine readable time stamp based on the unix epoch in UTC [s] </li> <li>runtime : time since last boot of the tom device [ms] </li> <li>latitude : Degrees latitude in decimal writing. For Trianel: [degree due North] </li> <li>longitude : Degrees longitude in decimal writing. For Trianel: [degree due East] </li> <li>elevation : elevation above mean sea level [m] </li> <li>rot_{x,y,z} : Rotational acceleration around the three cartesian axis {x,y,z} of the TOM box. [degree / s^2] </li> <li>acc_{x,y,z} : linear acceleration in each of the three cartesian axis {x,y,z} in the reference system of the TOM box [m/s^2] </li> <li>mag_{x,y,z} : magnetic field strength in each of the local cartesian TOM box axis {x,y,z} [micro-Tesla] </li> <li>roll : eulerian roll angle (angle around the x Axis of the tom box) [degree] </li> <li>pitch : eulerian pitch angle (angle around the y Axis of the tom box) [degree] </li> <li>yaw : eulerian yaw angle (angle around the z Axis of the tom box) [degree] </li> </ul> LIDAR wind data description A Leosphere WindCube LIDAR was mounted on the installation vessel Taillevent to record the atmospheric boundary layer during installation. The data recorded by the lidar was exported as csvs and provided by the vessel operator. The raw data includes the following parameters <ul> <li>epoch : time stamp as a unix epoch in UTC [s]</li> <li>wind_speed_N : The wind speed at the N'th return level [m/s]</li> <li>wind_dir_N : Wind direction at the N'th return level in the vessels reference frame [degree]</li> <li>wind_dir_N_corr : Wind direction at the N'th return level due North [degree due North]</li> <li>heigh_N : The height of the lidar return level [m] </li> </ul> Data is resampled to a 1 s return interval.  wave data description Wave data was recorded using a waverider wave buoy (DWR-G). The wave rider buoy was located at 54 00' 238'' degree North and 6 26' 553'' degree East. In decimals: 54.0031096 North, 6.4425532 East. Based on the raw data, sea state statistics were derived with a return period of 30 minutes. The data comes as csv, column oriented text files. The first line entails parameter names and units and is commented out by a hashbang (unix commentary). The data is a combination of two different data files as provided by the buoy: \*.HIS Data Text File (History of Spectrum parameters) and \*.HIW Data Text File (History of Wave Statistics). This results in the two timestamps in the data file because history of wave statistics data is available immediately after each measurement time period, whereas history of spectrum parameters take approx. 5 minutes to calculate by the buoy and thus have a slightly later time stamp. For actual postprocessing purposes, a third timestamp rounded to full half hour is used. Parameters included in the data files are: <ul> <li>epoch: number of seconds since 1st of January 1970 in UTC. Common time stamp format in computing. [s]</li> <li>Tp  Tp := 1 / fp, peak period, the frequency at which S(f) is maximal [s]</li> <li> Dirp  peak direction, the direction at f = fp [deg due North]</li> <li> Sprp  peak spread, the directional spread at f = fp [s]</li> <li> Tz  Tz := sqrt(m0 / m2), zero-upcross period [s]</li> <li> Hm0  Hm0 := 4*sqrt(m0), the significant waveheight [m]</li> <li> TI  TI := sqrt(m[-2] / m0), integral period [s]</li> <li> T1  T1 := m0 / m1, mean period [s]</li> <li> Tc  Tc := sqrt(m2 / m4), crest period [s]</li> <li> Tdw2  Tdw2 := sqrt(m[-1] / m1) [s]</li> <li> Tdw1  Tdw1 := sqrt(m[-1,2] / m0) [s]</li> <li> Tpc  Tpc := m[-2] * m1 / m0 ^ 2, calculated peak period [s]</li> <li> nu  nu := sqrt((T1 / Tz) ^ 2 - 1), band width parameter [-]</li> <li> eps  eps := sqrt(1 - (Tc / Tz) ^ 2), bandwidth parameter [-]</li> <li> QP  QP := 2 * m[1,2] / m0 ^ 2, Goda's peakedness parameter [-]</li> <li> Ss  Ss :=2 * pi / g * Hs / Tz ^ 2, significant steepness [-]</li> <li> TRef  TRef, reference temperature (25deg) [C]</li> <li> TSea  TSea, sea surface temperature [C]</li> <li> Bat  Bat, battery status (0..7) </li> <li> m[n]  m[n] := Integral from f=0 to f=Inf over S(f) * f ^ n </li> <li> m[n,2]  m[n,2] := Integral from f=0 to f=Inf over S(f) ^ 2 * f ^ n  </li> <li> Percentage  Percentage of data with no reception errors [%] </li> <li> Hmax  Height of the highest wave [cm] </li> <li> Tmax  Period of the highest wave) [s] </li> <li> H(1/10)  Average height of 10% highest waves [cm] </li> <li> T(1/10)  Average period of 10% highest waves [s] </li> <li> H(1/3)  Average height of 33% highest waves [cm] </li> <li> T(1/3)  Average period of 33% highest waves [s] </li> <li> Hav  Average height of all waves [cm] </li> <li> Tav  Average period of all waves) [s] </li> <li> Eps  bandwidth parameter </li> <li> #Waves  Number of waves</li> </ul> Note: the m's are moments of the power spectral density S(f).

opencc-by-4.0Oct 2020View details →

zenodo44/100

A Free Database of Head-Related Impulse Response Measurements in the Horizontal Plane with Multiple Distances

Head related impulse response measurements with the KEMAR dummy head performed in an anechoic chamber with a resolution of 1°. The impulse responses are provided for different distances and are accompanied by headphone compensation filters. For details have a look at README.md. The same measurement can be downloaded as MAT files at <a href="https://doi.org/10.5281/zenodo.4459911">https://doi.org/10.5281/zenodo.4459911</a> This dataset is further described in (see the PDF file) H. Wierstorf, M. Geier, A. Raake, S. Spors - A Free Database of Head-Related Impulse Response Measurements in the Horizontal Plane with Multiple Distances. In 130th AES Conv. 2011, eBrief 6.

opencc-by-nc-sa-4.0Jun 2016View details →

zenodo44/100

Skin response measurements

The objective of this study is to investigate the possibility to detect mental and light physical stress through the measurement of skin reflectance in the mm-wave/sub-THz band. Two frequency bands have been considered, 75-110 GHz (Band-I) and 325-500 GHz (Band-II), while the measurements have been performed in the three different locations, the arm, the dorsal side of the hand and the fingertip. Each transmitter and receiver set is equipped with a matched standard horn antenna operated at the selected frequency band. The transmitter and receiver used during the test are active mixers. To operate each active module a local oscillator (LO) is required. The PXA and the Agilent generators are used as LO for transmitter (TX) and receiver (RX) accordingly (Fig. 2). The output signal from the receiver (RX) is gathered by a spectrum analyzer and then post-processed. The entire lab bench is connected through GPIB and both LO generators are synchronized (10MHz). The measured reflected signal amplitude is recorded and post-process on a personal computer. The dataset refers to Laboratory test. The skin reflectance was measured during rest and after mental and physical stress. Physical stress was provoked using a dynamometer under 15 N of force for 5 minutes. Provocation of mental stress was achieved with the use of the Stroop Test [11] for 15 minutes. A stress measurement was always preceded by a resting period of at least 15 minutes. Three hand locations have been considered, (a) arm, (b) hand and (c) finger. The datasets are related to the open accessible publication "Human Physical Condition RF Sensing at THz range".

opencc-by-nc-nd-4.0Sep 2016View details →

zenodo44/100

Spatially-localized X-ray scattering and X-ray microtomography measurements on Moso bamboo

Spatially-localized X-ray scattering and X-ray microtomography measurements on Moso bamboo This data set is originally used in: Ahvenainen, P., Dixon, P. G., Kallonen, A., Suhonen, H., Gibson, L. J., & Svedström, K. (2017). Spatially-localized bench-top X-ray scattering reveals tissue-specific microfibril orientation in Moso bamboo. Plant Methods. 13:5 DOI: 10.1186/s13007-016-0155-1 This data set includes measurements on Moso bamboo (Phyllostachys edulis) performed with two separate set-ups at the Department of Physics, University of Helsinki as described in the above open-access publication. The X-ray microtomography (XMT) measurements, X-ray diffraction tomography (XDT) and localized X-ray scattering (LXS) are done with set-up 1. In LXS, the region-of-interest is selected from a tomographic reconstruction slice based on the XMT measurement using a small X-ray beam (diameter: 200 µm). Additional wide-angle X-ray scattering (WAXS) measurements are conducted with set-up 2 using a larger X-ray beam (diameter approx. 1 mm). The two-dimensional scattering patterns (Pilatus 1M hybrid pixel array detector) and tomographic reconstruction slices obtained with set-up 2 are stored as TIFF-images (.tif). The two-dimensional scattering patterns (MAR345 image plate detector) obtained with set-up 2 are stored as 32-bit RAW files (unsigned integers, 2300 columns, 2300 rows). The novel combined WAXS/XMT set up (set-up 1) is first presented in: Suuronen, J.-P., Kallonen, A., Hänninen, V., Blomberg, M., Hämäläinen, K., & Serimaa, R. (2014). Bench-top X-ray microtomography complemented with spatially localized X-ray scattering experiments. Journal of Applied Crystallography, 47(1), 471–475. doi:10.1107/S1600576713031105 Any queries related to the data set or the related Plant Methods article may be directed to the first author by email: Patrik Ahvenainen, PhD; patrik.ahvenainen@alumni.helsinki.fi

opencc-by-4.0Jan 2017View details →

zenodo44/100

Dataset of experimental measurements for "Demonstration of quantum advantage in machine learning"

Dataset of experimental measurements for "Demonstration of quantum advantage in machine learning", npj Quantum Information 3, Article number: 16 (2017).

opencc-by-4.0Feb 2017View details →

zenodo44/100

Lyman-alpha forest simulations used for the measurement of the smoothing scale of the intergalactic medium

This repository contains data from the hydrodynamic and dark-matter simulations used in Rorai et al.2017 to measure the pressure smoothing scale of the intergalactic medium (IGM) using the lyman alpha forest from close quasar pairs. The data includes : synthetic spectra (at z~2,2.4,3,3.6) of the transmitted lya flux from a grid of hydrodynamic model of the IGM assuming various thermal and reionization histories; Velocity and density sight lines from a dark matter simulation (at the same redshifts), with different values of the smoothing parameters, which can be used to calculate the lyman alpha flux for the desired values of the thermal parameters (as well as of the mean flux); More information can be found in the README file

opencc-by-4.0Feb 2017View details →

zenodo44/100

OpenUP survey on researchers' current perceptions and practices in peer review, impact measurement and dissemination of research results

OpenUP project (http://openup-h2020.eu/) conducted a survey to capture current perceptions and practices in peer review, dissemination of research results and impact measurement among European researchers. The survey was coducted between 20 January and 23 February 2017. It consisted of four sections. The first section asked a series of questions on the respondents’ scientific discipline, career stage, gender and other characteristics. The following sections asked a series of questions on peer review practices, dissemination of research results and impact measurement/use of altmetrics. The questionnaire was collaboratively prepared by the OpenUP consortium. The survey was implemented via surveygizmo tool (https://www.surveygizmo.com/). Invitations to participate were sent to a random sample of researchers from arXiv, Pubmed and RePEc. The OpenUP team mined researchers’ contact details from these platforms. The OpenUP project team made efforts to further boost the repondent sample for certain underrepresented areas through the DARIAH website, THESIS network, EURODOC, AIMS portal, the Parthenos community and other channels. The survey targeted researchers from the EU-28, Switzerland and Norway. The goal was to get around 1,000 responses. In total, there were 976 completed response and completion rate was 72.4%. The attached documents include the questionnaire and the dataset. In the dataset (cvs file) the top row contains numbered questions that correspond to the numberring in the questionnaire (word file). The data was exported as an excel file, anonymised by creating respondent IDs and IP data were deleted. The file was then converted to CSV.

opencc-by-4.0Apr 2017View details →

zenodo44/100

GHOST: A globally harmonised dataset of surface atmospheric composition measurements

<div> <div>GHOST: Globally Harmonised Observations in Space and Time, represents one of the biggest collection of harmonised measurements of atmospheric composition at the surface. In total, ~10 billion measurements from 1970-2025, of ~600 different components, from ~40 reporting networks, are compiled, parsed, and standardised. Components processed include gaseous species, total and speciated particulate matter, and aerosol optical properties.</div> <div>The main goal of GHOST is to provide a dataset that can serve as a basis for the reproducibility of model evaluation efforts across the community. Exhaustive efforts have been made towards standardising almost every facet of provided information from the major public reporting networks, saved in 21 data variables, and 163 metadata variables. Extensive effort in particular is put towards the standardisation of measurement process information, and station classifications. Extra complementary information is also associated with measurements, such as metadata from various popular gridded datasets (e.g. land use), and temporal classifications per measurement (e.g. day / night). A range of standardised network quality assurance flags are associated with each individual measurement. GHOST own quality assurance is also performed and associated with measurements. Measurements prefiltered by some default GHOST quality assurance are also provided.  </div> <h3>Data Access </h3> <div>The data processed in version 1.5.1 was a result of research undertaken in two separate projects. The processing and creation of new aerosol optical property products was done within the FOCI project, and the processing and creation of precipitation chemistry and wet deposition products was funded by the World Meteorological Organization for the Measurement-Model Fusion for Total Global Atmospheric Deposition WMO Initiative. The processed data is designed to be complementary to the data provided in version 1.5 of GHOST.  </div> <div> </div> <div>The data is separated out per network, per temporal resolution, per component, and is saved as netCDF4 files, per year and month. There is additionally one synthetic network entitled "GHOST", which aggregates data across all networks. The dataset is compressed as .zip files per network. Beneath each network, collections of files per temporal resolution, per component, are compressed as tar.xz files.</div> <div> </div> <div>Each network .zip file can be decompressed via the following syntax: unzip [network].zip</div> <div> </div> <div>Component tar.xz files can be decompressed via the following syntax: tar -xf [component].tar.xz</div> <h3>How to Use</h3> Inside the GHOST dataset are a plethora of variables, thus it can difficult to fully exploit the extent of the available information. For this reason a companion publication has been written, detailing every aspect of the GHOST dataset: https://doi.org/10.5194/essd-2023-397 <div>If you have any other doubts of queries regarding the dataset, please email: dene.bowdalo@bsc.es</div> <h3>How to Cite</h3> If you plan to use this work please kindly cite both this dataset and the describing publication: </div> <div> <div>Bowdalo, D.: GHOST: A globally harmonised dataset of surface atmospheric composition measurements, Zenodo [data set], https://doi.org/10.5281/zenodo.10637449, 2024.</div> <div>Bowdalo, D., Basart, S., Guevara, M., Jorba, O., Pérez García-Pando, C., Jaimes Palomera, M., Rivera Hernandez, O., Puchalski, M., Gay, D., Klausen, J., Moreno, S., Netcheva, S., and Tarasova, O.: GHOST: A globally harmonised dataset of surface atmospheric composition measurements, Earth Syst. Sci. Data, 16, 4417–4495, https://doi.org/10.5194/essd-16-4417-2024, 2024.</div> <h3>Acknowledgements</h3> <div>We gratefully acknowledge all data providers for the substantial work done in establishing and maintaining the measuring stations that provide the data contained in this dataset. We would also like to warmly thank all data providers who met with GHOST authors through this work, and for all support given, from helping resolve data rights issues, to giving suggestions for improvements.</div> <div> </div> <div>We acknowledge the computing resources of MareNostrum, and the technical support provided by the Barcelona Supercomputing Center (AECT-2020-1-0007, AECT-2021-1-0027, AECT-2022-1-0008, and AECT-2022-3-0013). We also acknowledge the Red Temática ACTRIS España (CGL2017-90884-REDT), and the H2020 project ACTRIS IMP (\#871115).</div> <div> </div> <div>The processing and creation of new aerosol optical property products was funded by EU HORIZON EUROPE under grant agreement no. 101056783 (FOCI project), and the processing and creation of precipitation chemistry and wet deposition products was funded by the World Meteorological Organization for the Measurement-Model Fusion for Total Global Atmospheric Deposition WMO Initiative.  </div> <div> </div> <div>The research leading to the creation of this dataset has also received funding from the grant RTI2018-099894-BI00 funded by MCIN/AEI/ 10.13039/501100011033 (BROWNING), the EU H2020 Framework Programme under grant agreement No. GA 821205 (FORCES), the European Research Council under the Horizon 2020 research and innovation programme through the ERC Consolidator Grant grant agreement No. 773051 (FRAGMENT), the AXA Research Fund (AXA Chair on Sand and Dust Storms at the Barcelona Supercomputing Center), and the Department of Research and Universities of the Government of Catalonia through the Atmospheric Composition Research Group (code 2021 SGR 01550).</div> </div>

opencc-by-4.0Feb 2024View details →

zenodo44/100

TCOM-HCl : Daily global gap-free stratospheric hydrogen chloride profile data set based on TOMCAT CTM and Occultation Measurements

Methodology:   The TOMCAT simulation was conducted at a T64L32 resolution, consistent with previous work by Dhomse et al. (2021, 2022), covering the period from 2000 to 2024. These simulations utilized ERA-5 reanalysis data. <h3>HCl Profile Processing and Bias Correction</h3> Collocated HCl profiles are organized into five distinct latitude bins: <ul> <li> NH polar: 90∘N - 50∘N </li> <li> NH mid-lat: 20∘N - 70∘N </li> <li> Tropics: 40∘S - 40∘N </li> <li> SH mid-lat: 70∘S - 20∘S </li> <li> SH polar: 90∘S - 50∘S </li> </ul> Initially, differences between TOMCAT and satellite measurements (primarily ACE-FTS data) are calculated for each zonal bin across 51 height levels (ranging from 10,km to 60,km). Separate XGBoost regression models are then trained for these HCl differences at each height level within a given latitude bin. These trained models are subsequently used to estimate HCl bias corrections for all daytime TOMCAT grids (9132 days), specifically sampled at 1:30 PM local time at the equator. This yields grid-specific bias corrections that are applied to the original TOMCAT profiles. Height-resolved HCl profile data are then interpolated onto 28 standard pressure levels (from 300,hPa to 0.1,hPa), using pressure levels directly from the TOMCAT grids. For overlapping latitude bins, values are averaged to ensure smoother fields near boundary regions. <h3>Data Files</h3> The dataset includes two files containing daily mean zonal mean HCl profiles: <ul> <li> <code>zmhcl_TCOM_hlev_T2Dz_2000-2024_V1.1.nc</code>: Contains height level data (10,km to 60,km). </li> <li> <code>zmhcl_TCOM_plev_T2Dz_2000-2024_V1.1.nc</code>: Contains pressure level data (300,hPa to 0.1,hPa). </li> </ul> <h3>Reference Publication</h3> This methodology, incorporating only ACE-FTS data and various minor algorithmic developments, is based on the following publication: Dhomse, S. S. and Chipperfield, M. P.: Using machine learning to construct TOMCAT model and occultation measurement-based stratospheric methane (TCOM-CH4) and nitrous oxide (TCOM-N2O) profile data sets, Earth Syst. Sci. Data, 15, 5105–5120, <a title="null" href="https://doi.org/10.5194/essd-15-5105-2023">https://doi.org/10.5194/essd-15-5105-2023</a>, 2023

opencc-by-4.0Feb 2023View details →

zenodo44/100

TCOM-H2O: TOMCAT CTM and Occultation Measurements based daily zonal stratospheric H2O profile dataset [1991-2021] constructed using machine-learning.

Methodology:   The TOMCAT simulation was conducted at a T64L32 resolution, consistent with previous work by Dhomse et al. (2021, 2022), covering the period from 2000 to 2024. These simulations utilized ERA-5 reanalysis data. <h3>H2O Profile Processing and Bias Correction</h3> Collocated H2O profiles are organized into five distinct latitude bins: <ul> <li> NH polar: 90∘N - 50∘N </li> <li> NH mid-lat: 20∘N - 70∘N </li> <li> Tropics: 40∘S - 40∘N </li> <li> SH mid-lat: 70∘S - 20∘S </li> <li> SH polar: 90∘S - 50∘S </li> </ul> Initially, differences between TOMCAT and satellite measurements (primarily ACE-FTS data) are calculated for each zonal bin across 51 height levels (ranging from 10,km to 60,km). Note that TOMCAT may not accurately capture H2O evolution post-HTHH eruption due to the sparse spatial coverage of ACE measurements, which limits training data. Separate XGBoost regression models are then trained for these H2O differences at each height level within a given latitude bin. These trained models are subsequently used to estimate H2O bias corrections for all daytime TOMCAT grids (9132 days), specifically sampled at 1:30 PM local time at the equator. This yields grid-specific bias corrections that are applied to the original TOMCAT profiles. Height-resolved H2O profile data are then interpolated onto 28 standard pressure levels (from 300,hPa to 0.1,hPa), using pressure levels directly from the TOMCAT grids. For overlapping latitude bins, values are averaged to ensure smoother fields near boundary regions. We acknowledge the inherent dry biases in the original TOMCAT H2O profiles, largely because the TTL entry mixing ratios are based on a simplistic sinusoidal seasonal cycle, which omits the H2O enhancement contributed by tropical convective clouds. <h3>Data Files</h3> The dataset includes two files containing daily mean zonal mean H2O profiles: <ul> <li> <code>zmh2o_TCOM_hlev_T2Dz_2000-2024_V1.1.nc</code>: Contains height level data (10,km to 60,km). </li> <li> <code>zmh2o_TCOM_plev_T2Dz_2000-2024_V1.1.nc</code>: Contains pressure level data (300,hPa to 0.1,hPa). </li> </ul> <h3>Reference Publication</h3> This methodology, incorporating only ACE-FTS data and various minor algorithmic developments, is based on the following publication: Dhomse, S. S. and Chipperfield, M. P.: Using machine learning to construct TOMCAT model and occultation measurement-based stratospheric methane (TCOM-CH4) and nitrous oxide (TCOM-N2O) profile data sets, Earth Syst. Sci. Data, 15, 5105–5120, <a title="null" href="https://doi.org/10.5194/essd-15-5105-2023">https://doi.org/10.5194/essd-15-5105-2023</a>, 2023.

opencc-by-4.0May 2023View details →

zenodo44/100

TCOM-O3: TOMCAT CTM and Occultation Measurements based daily zonal stratospheric ozone profile dataset [1991-2021] constructed using machine-learning

Methodology:  TOMCAT simulation is performed at T64L32 resolution for the 2000-2024 time period. Collocated Ozone (O3) profiles are divided in five latitude bins: SH polar (90S-50S), SH mid-lat (70S-20S), tropics (40S-40N), NH mid-lat (20N-70N) and NH polar (50N-90N). Initially, model-measurement  differences are calculated for each zonal bins (51 height levels, 10km to 60km). Note that if enough ACE measurements are not avaliable for a particular level then data is purely based on TOMCAT simulated output field. Separate XGBoost regression models are trained for the  differences between TOMCAT and measurements at each level for a given latitude bin. XGBoost model is then used to estimate error corrections for all the TOMCAT grids.  TOMCAT output sampled at 1.30 pm local time at the equator. Estimated corrections for a given model grid that are added to the original TOMCAT simulated day and night time ozone profiles. Height resolved data are then interpolated on 28-pressure levels (300 - 0.1hPa). For overlapping latitude bins, we use averages and then calculate daily zonal mean values.  For more details see attached presentation. Previous version use both HALOE and ACE data. Here only ACE data is used (hence starting date is 01 January 2000). PDF file shows comparison between v1.0 and v1.1 as well as TOMCAT data. Dataset also includes two files containing daily mean zonal mean hydrogen fluoride  profiles on height (10-60 km) and pressure (300-0.1 hPa) levels: zmo3_TCOM_hlev_T2Dz_2000_2024.nc – height level data (10 to 60 km) zmo3_TCOM_plev_T2Dz_2000_2024.nc – pressure level data (300 to 0.1 hPa) Daily 3D profiles on height and pressure levels would be made available on request.

opencc-by-4.0Feb 2023View details →

zenodo44/100

TOMCAT CTM and Occultation measurement-based Stratospheric CFC12 (TCOM-CFC12) profile data set

TOMCAT CTM and Occultation measurement-based Stratospheric CFC12 (TCOM-CFC12) profile data set   Sandip S. Dhomse  School of Earth and Enviro, University of Leeds, Leeds, UK National Centre for Earth Observations, University of Leeds, Leeds, UK  email: s.s.dhomse@leeds.ac.uk  Methodology:  TOMCAT simulation is performed at T64L32 resolution for the 2000-2024 time period. Collocated CFC12 (CF2Cl2)  profiles are divided in five latitude bins: SH polar (90S-50S), SH mid-lat (70S-20S), tropics (40S-40N), NH mid-lat (20N-70N) and NH polar (50N-90N). Initially, model-measurement differences are calculated for each zonal bins (51 height levels, 10km to 60km). Separate XGBoost regression models are trained for the differences between TOMCAT and measurements at each level for a given latitude bin. XGBoost model is then used to estimate error corrections for all the TOMCAT grids. Estimated corrections for a given model grid that are added to the original TOMCAT simulated daily (at 1.30 local time) CFC-12 profiles. Height resolved data are then interpolated on 28-pressure levels (300 - 0.1hPa). For overlapping latitude bins, we use averages and then calculate daily zonal mean values.  For more details see attached presentation. Dataset also includes two files containing daily mean zonal mean CFC-12 profiles on height (10-60 km) and pressure (300-0.1 hPa) levels (9132 days/64 latitudes): zmcfc12_TCOM_hlev_T2Dz_2000-2024_V1.1.nc – height level data (10 to 60 km) zmcfc12_TCOM_plev_T2Dz_2000-2024_V1.1.nc – pressure level data (300 to 0.1 hPa) Daily 3D profiles on height and pressure levels would be made available on request. Xarrays “resample” can be used to get monthly means.

opencc-by-4.0Jun 2024View details →

zenodo44/100

Hybrid Solar Panel Real Measurements in Switzerland

<h3>Measurements of a hybrid solar panel</h3> Datasheet of the hybrid PV panel (<a title="Datasheet" href="https://cdn.enfsolar.com/Product/pdf/Crystalline/55adc587c2506.pdf" target="_blank" rel="noopener">Here</a>) These measurements have taken place in two places in Valais, Switzerland. One is "Granges" in longitude: 7.4649965° and latitude: 46.2647793° and the other is "Sion" in longitude: 7.364832° and latitude: 46.227372°.

opencc-by-4.0Jun 2024View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated datasets

Allen Brain Atlas

Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

Annotated Behaviour and Observability Dataset (ABODe)

ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

DANDI Archive for NWB datasets

DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

International Brain Laboratory public data

The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

OpenNeuro

OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record