Find research datasets worth reusing

opencc-by-sa-4.0Apr 2024View details →

Wind Turbine SCADA Data For Early Fault Detection

This dataset is published together with the <a href="https://doi.org/10.3390/data9120138">paper</a> "CARE to Compare: A real-world dataset for anomaly detection in wind turbine data" which explains the dataset in detail and defines the CARE score that can be used to evaluate anomaly detection algorithms on this dataset. When referring to this dataset, please cite the paper mentioned in the related work section.  The data consists of 95 datasets, containing 89 years of SCADA time series distributed across 36 different wind turbines from the three wind farms A, B and C. The number of features depends on the wind farm; Wind farm A has 86 features, wind farm B has 257 features and wind farm C has 957 features.  The overall dataset is balanced, as 45 out the 95 datasets contain a labeled anomaly event that leads up to a turbine fault and the other 50 datasets represent normal behavior. Additionally, the quality of training data is ensured by turbine-status-based labels for each data point and further information about some of the given turbine faults are included. The data for Wind farm A is based on data from the EDP open data platform (https://www.edp.com/en/innovation/open-data/data),  and consists of 5 wind turbines of an onshore wind farm in Portugal.  It contains SCADA data and information derived by a given fault logbook which defines start timestamps for specified faults.  From this data 22 datasets were selected to be included in this data collection.  The other two wind farms are offshore wind farms located in Germany. All three datasets were anonymized due to confidentiality reasons for the wind farms B and C. Each dataset is provided in form of a csv-file with columns defining the features and rows representing the data points of the time series. Files More detailed information can be found in the included README-file. Notes In wind farm A status_type_id labels can be ignored while evaluating prediction time frames of error events with metrics like the CARE-score since the status_type_id is of wind farm A is based on the EDP failure logbook and it is intended to be used for filtering of the training data. Version Changes: Version 5 -> 6: <ul> <li>Changed unit of sensor_40 and sensor_61 for wind farm C to hPa instead of bar. This unit error became obvious when looking at the data and comparing it to the standard air pressure.</li> <li>Edited event_description of events 34, 7 and 19 to high temperature in transformer cell.</li> <li>Changed date in event description of event 44 since it was not affected by the change in the date anonymization procedure from version 2.</li> <li>Changed date in event description of event 47 since it was not affected by the change in the date anonymization procedure from version 2 and edited the description text</li> <li> Changed date format in event_info files to match the date format in the dataset files.</li> <li>Fixed typo in Readme</li> <li>Re-added Readme files</li> </ul> Version 4->5: Corrections to labels were made: <ul> <li>Previously missing status_type_id 4 labels were added to datasets in Wind Farm A. </li> <li>Event 51 from Wind Farm A was wrongly labeled as a normal event. With the newly added status_type_id 4 occurences, it is to be considered an anomaly event due to a gearbox bearing damage within the prediction data.</li> <li>Wind Farm A no longer contains status_type_id 5. All occurences of status_type_id 5 have been changed to 0 and are considered normal time stamps. This change is done, because status_type_id 5 was set as a result of a wind speed and power analysis, flagging potential anomalous data. This is not based on a fixed ground truth, so status_type_id 5 was removed. For Wind Farms B and C status_type_id 5 is still valid since it is based on real SCADA-status codes.</li> <li>The event_info.csv files now contain an additional column 'asset_id'.</li> </ul> Version 3->4: <ul> <li>The change of the timestamp anonymization lead to duplicate timestamps when transitioning from a leap year to 2022. This is now fixed in Version 4.</li> </ul> Version 2->3: <ul> <li>In version 2 timestamp changes were not consistent with the timestamps in the event-info-files. Version 3 fixes this.</li> </ul> Version 1->2: <ul> <li>Version 2 contains one deviation from version 1 regarding the anonymization procedure. Instead of shifting the timestamps of each sub-dataset by a random number of years, the size of the time shift is now determined to be the number of years so that each sub-dataset starts in 2022. This change is made to make the timestamp anonymization more consistent and to avoid future timestamps being present within the data.</li> </ul>

opencc-by-4.0Oct 2023View details →

The Dynamics of the India-Eurasia Collision: Faulted Viscous Continuum Models Constrained by High-Resolution Sentinel-1 InSAR and GNSS Velocities

Velocity field for the India-Eurasia collision zone from Sentinel-1 InSAR and GNSS data Citations: [1] Jin Fang, Gregory A Houseman, Tim J Wright, Lynn A Evans, Tim J Craig, John R Elliott and Andy Hooper (2023). The Dynamics of the India-Eurasia Collision: Faulted Viscous Continuum Models Constrained by High-Resolution Sentinel-1 InSAR and GNSS Velocities [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.10053499 [2] Jin Fang, Gregory A Houseman, Tim J Wright, Lynn A Evans, Tim J Craig, John R Elliott and Andy Hooper (2024). The Dynamics of the India-Eurasia Collision: Faulted Viscous Continuum Models Constrained by High-Resolution Sentinel-1 InSAR and GNSS Velocities, Journal of Geophysical Research: Solid Earth, https://doi.org/10.1029/2023JB028571 More details about the methodology to generate the velocity field can be found in Wright et al. (2023): [3] Tim J Wright, Greg Houseman, Jin Fang, Yasser Maghsoudi, Andy Hooper, John Elliott, Lynn Evans, Milan Lazecky, Qi Ou, Barry Parsons, Chris Rollins, Lin Shen, Hua Wang (2023). High-resolution geodetic strain rate field reveals dynamics of the India-Eurasia collision, submitted to Science, preprint available at https://doi.org/10.31223/X5G95R.

opencc-by-4.0Nov 2023View details →

Slow slip as an indicator of fault stress criticality

This dataset contains simulated data used in Lambert (submitted): Slow slip as an indicator of fault stress criticality.

opencc-by-4.0Oct 2023View details →

Coseismic frictional heating with concomitant hydrothermal fluid circulation revealed by rock magnetic properties of fault rocks from the rupture of the 2008 Wenchuan earthquake, China

This repository contains the rock magnetic data associated with the manuscript entitled "Coseismic frictional heating with concomitant hydrothermal fluid circulation revealed by rock magnetic properties of fault rocks from the rupture of the 2008 Wenchuan earthquake, China" by Yan et al. published in Geochemistry, Geophysics, Geosystems, 24, e2023GC011223. https://doi.org/10.1029/2023GC011223

opencc-by-4.0Nov 2023View details →

Measurements of fault slip associated with the 2023 Superstition Hills Fault slow slip event

Creepmeter time series, global navigation satellite system (GNSS) measurements, line-of-sight Sentinel-1A interferograms with atmospheric corrections, and field measurements of slip along the Superstition Hills Fault in 2023 due to slow slip.

opencc-by-4.0Nov 2023View details →

Chesley et al., 2023 - Gofar Oceanic Transform Fault CSEM data from fault-perpendicular profiles (collected 2022)

This repository contains processed and edited controlled-source electromagnetic amplitude and phase data from the Gofar oceanic transform fault with corresponding bathymetry files. The files beginning "dataFile.." are the amplitudes and phases and the files beginning "topo..." are the bathymetry information. Each profile in this repository crosses the fault approximately perpendicularly.

dryad36/100

Frictional properties of feldspar-chlorite altered gouges and implications for fault reactivation in hydrothermal systems

As particularly common minerals in granites, the presence of feldspar and altered feldspar-chlorite gouges at hydrothermal conditions have important implications in fault strength and reactivation. We present laboratory observations of frictional strength and stability of feldspar (K-feldspar and albite) and altered feldspar-chlorite gouges under conditions representative of deep geothermal reservoirs to evaluate the impact on fault stability. Velocity-stepping experiments are performed at a confining stress of 95 MPa, pore pressures of 35–90 MPa and temperatures of 120–400°C representative of in situ conditions for such reservoirs. Our experiment results show that the feldspar gouge is frictionally strong (μ~0.71) at all experimental temperatures (~120–400℃) but transitions from velocity-strengthening to velocity-weakening at T>120°C. Increasing the pore pressure increases the friction coefficient (~0.70-0.87) and the gouge remains velocity weakening, but this weakening decreases as pore pressures increase. The presence of alteration-sourced chlorite leads to a transition from velocity weakening to velocity strengthening in the mixed gouge at experimental temperatures and pore pressures. As a ubiquitous mineral in reservoir rocks, feldspar is shown to potentially contribute to unstable sliding over ranges in temperature and pressure typical in deep hydrothermal reservoirs. These findings emphasize that feldspar minerals may increase the potential for injection-induced seismicity on pre-existing faults if devoid of chlorite alteration.

opencc-zeroDec 2023View details →

opencc-by-4.0Dec 2023View details →

Fault Detection and Inventory Management in Manufacturing with Plastic Bricks: A Dataset

Dataset for Smart Manufacturing The dataset contains images of plastic bricks showcasing various colors, shapes, and minor surface damages, designed to represent the use-cases of quality classification and inventory management in manufacturing. The use cases are separated and include 3 categories for quality classification and 24 categories for inventory classification. The data is analyzed in the publication titled "Demonstrating Computer Vision to Small- and Medium-sized Enterprises in Manufacturing: Towards Overcoming Costs and Implementation Challenges". The associated research explores the development of a simple computer vision demonstrator and its demonstration to small- and medium-sized enterprises.  Structure of Files <blockquote> quality_classification (224 images)     defect     defect_free     empty inventory_classification (2732 images)    beige_large    beige_small    blue_bright_large    21 further categories (colour_shape) </blockquote>

opencc-by-4.0Jul 2023View details →

A Large Fault Partially Reactivated During Two Contiguous Seismic Sequences in Central Italy: The Role of Geometrical and Frictional Heterogeneities

Moment tensor catalog for events with M > 3.0, that occurred between January 2009 and April 2021, in Campotosto area, Italy. Moments tensor were calculated by applying the Time Domain Moment Tensor technique, originally proposed by Dreger and Helmberger (1993) and Pasyanos et al. (1996) and successively implemented at INGV by Scognamiglio et al. (2009). The catalog includes: Location of events: time, depth, lat and lon The moment magnitude: Mw The double-couple value: DC The Variance Reduction value: VR The six moment tensor components: Mxx, Mxy, Mxz, Myy, Myz, Mzz The orientation of nodal planes: strike1, dip1, rake1, strike2, dip2, rake2

Displacement time series from GNSS stations in the Alto Tiberina Fault area (Central Italy)

The files report the position time-series of GNSS stations deployed in the Alto Tiberina Fault area (Central Italy). Columns are: Time, E, N, Se, Sn, Ren, U, Su, Reu, Rnu, site, long, lati, representing, respectively, epoch (in decimal years), displacement in the East component (in mm), displacement in the North component (in mm), uncertainty (one standard deviation) of the East component (in mm), uncertainty (one standard deviation) of the North component (in mm), correlation between the East and North components, displacement in the Up component (in mm), uncertainty (one standard deviation) of the Up component (in mm), correlation between the East and Up components, correlation between the North and Up components, Station ID (four letters), Longitude of the station (°), Latitude of the station (°).

What controls active faulting in Tertiary and Quaternary sequences ?

Structural, geomechanical and XRD datasets obtained from outcrop investigations of Galera Fault zone located in the Guadix-Baza basin, SE Spain is presented here.

Reducing the runtime of fault-tolerant quantum simulations in chemistry through symmetry-compressed double factorization

Data repository for "Reducing the runtime of fault-tolerant quantum simulations in chemistry through symmetry-compressed double factorization" <a href="https://arxiv.org/abs/2403.03502" target="_blank" rel="noopener">arXiv:2403.03502</a>.

Supplementary Data for Study on Strain Localization of Strike-Slip Fault Systems

Point displacement measurements of fault slip from geodetic imaging and field surveying used to estimate off-fault deformation.

Fault zone material files for dynamic rupture modeling of the 2019 Ridgecrest earthquakes

This repository contains the material files used to add a low-velocity fault zone to a dynamic rupture model of the 2019 Ridgecrest sequence (Taufiqurrahman et al., 2023, Nature).

Supplemental Material for "Anomalous Shear Stress Variation in Wet Granular Medium: Implications for Landslide Lateral Faults"

Dataset and Constrained Parameters for "Anomalous Shear Stress Variation in Wet Granular Medium: Implications for Landslide Lateral Faults" by Chang et al. This dataset comprises 64 rheological experiments, which have been encapsulated in four experimental groups: Full Height, Half Height, Half Height & Load, and Ethanol-Water.  The experiments were conducted using a double-cylinder geometry on three-phase granular media to simulate the lateral faults of slow-moving landslides. The dataset includes Mechanical Data for time-evolution measurements of shear stress, fluid volume fraction under varying conditions, and Image Data for velocity fields derived from PIV (Particle Image Velocimetry) analysis. In addition, it provides constrained parameters, such as steady-state shear stress and the characteristics of the flow structure. Article information: <div> <div>Chang, C., Ohno, K., Schulz, W. H., & Yamaguchi, T. (2025). Anomalous Shear Stress Variation in Wet Granular Medium: Implications for Landslide Lateral Faults. Geophysical Research Letters, 52(7), e2024GL113816. <a href="https://doi.org/10.1029/2024GL113816">https://doi.org/10.1029/2024GL113816</a></div> </div>

Data for "Shear Strain Evolution Spanning the 2020 Mw6.8 Elazığ and 2023 Mw7.8/Mw7.6 Kahramanmaraş Earthquake Sequence along the East Anatolian Fault Zone" manuscript

Data necessary to support the analysis presented in "Shear Strain Evolution Spanning the 2020 Mw6.8 Elazığ and 2023 Mw7.8/Mw7.6 Kahramanmaraş Earthquake Sequence along the East Anatolian Fault Zone" manuscript

opencc-by-4.0Dec 2024View details →

Relationship between rupture length and magnitude of oceanic transform fault earthquakes

We provide here the supplementary material to Relationship between rupture lengths and magnitudes of oceanic transform fault earthquakes, by Guilherme de Melo, Ingo Grevemeyer, Dietrich Lange, Dirk Metz, and Heidrun Kopp.

opencc-by-4.0Jul 2023View details →

Displacement Data for "The Central Mindoro Fault: A Sinistral Fault Within the Translational Boundary Between the Palawan Microcontinental Block and the Philippine Mobile Belt"

This dataset contain information on reference features, locations, V/H measurements, and sense of displacement along the Central Mindoro Fault (Philippines).

opencc-by-4.0Sep 2023View details →

Supporting dataset -A for "Crystallization of FAPbI3: polytypes and stacking faults"

This dataset contains <ol> <li>XYZ trajectories of 2H-polytype-3R phase phase transition for 1296 atoms and 1728 atom suspercell of FAPbI3</li> <li>Predicted polytypes and their mixtures from MD simulations, for example 12H polytype of FAPbI3</li> </ol>

Input and Output Data for local earthquake tomography in the central Dead Sea Fault using PyVoroTomo

Data to reproduce wave velocity models for the central DSF. eventsAndArrivals.h5 - 2 csv files (keys: events, arrivals) stations_sub.h5 - csv file containing station data gitter.csv - 1D velocity model by Gitterman et al. (2002) 3d_Vp_Vs_VpVs_models.nc - velocity models for vp, vs, and vp/vs and their uncertainty. relocated seismicity.h5 - relocated seismicity, arrivals used, and stations used (keys: events, arrivals, stations)