cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6811595
title: Dataset for "Emerging tremors and increasing seismic noise precede micro-earthquakes triggered in a fluid-activated shale fault slip experiment (2015, Mt Terri URL, Switzerland)"

description: This dataset contains the raw data of the injection experiment, performed in the Mt Terri Underground Platform in 2015 and used in the article: <strong>De Barros, L., </strong>Guglielmi, Y., F. Cappa, C. Nussbaum, J. Birkholzer, 2023. Induced microseismicity and tremor signatures illuminate different slip behaviors in a natural shale fault reactivated by a fluid pressure stimulation (Mont Terri), <em>Geophysical Journal International</em>, 10.1093/gji/ggad231<br> <br> From a horizontal gallery, three vertical boreholes allowed the deployment of an injection probe (called SIMFIP; Guglielmi et al., 2014) and the monitoring sensors in the upper compartment of a N50°-60°SE fault zone. The 2.4 m long injection chamber of the SIMFIP probe was centered at 340.6 m depth, where a 3D displacement sensor was anchored on the borehole walls. A second SIMFIP probe is located 3.1 m northwest of the injection at a depth of 337.65 m, with another deformation sensor. Both deformation sensors measured the full strain tensor thanks to a Bragg optic fiber network, jointly with a fluid pressure sensor. A third borehole, located 2 m north of the monitoring probe, was dedicated to seismic monitoring. Two sets of collocated sensors, composed of a vertical geophone, a 3C accelerometer and an acoustic sensor, were positioned 9 m apart, above and below the main fault zone. These seismic sensors have a flat response in the ranges 0.01-0.5 kHz, 0.01-4 kHz and 0.5-10 kHz, respectively. Finally, the flowrate and pressure were also measured at the injection pump, located in the gallery.<br> For more details on the injection, we refer the reader to:<br> • Jeanne, P., Guglielmi, Y., Rutqvist, J., Nussbaum, C., Birkholzer, J., 2018. Permeability Variations Associated With Fault Reactivation in a Claystone Formation Investigated by Field Experiments and Numerical Simulations. J. Geophys. Res. Solid Earth 123, 1694–1710. https://doi.org/10.1002/2017JB015149<br> • Guglielmi, Y., Nussbaum, C., Cappa, F., De Barros, L., Rutqvist, J., Birkholzer, J., 2021. Field-scale fault reactivation experiments by fluid injection highlight aseismic leakage in caprock analogs: Implications for CO2 sequestration. Int. J. Greenh. Gas Control 111, 103471. https://doi.org/10.1016/j.ijggc.2021.103471<br> • Guglielmi, Y., Nussbaum, C., Jeanne, P., Rutqvist, J., Cappa, F., Birkholzer, J., 2020. Complexity of Fault Rupture and Fluid Leakage in Shale: Insights From a Controlled Fault Activation Experiment. J.Geophys. Res. Solid Earth 125, e2019JB017781. https://doi.org/10.1029/2019JB017781<br> • Guglielmi, Y., Cappa, F., Lançon, H., Janowczyk, J.B., Rutqvist, J., Tsang, C.F., Wang, J.S.Y., 2014. ISRM Suggested Method for Step-Rate Injection Method for Fracture In-Situ Properties (SIMFIP): Using a 3-Components Borehole Deformation Sensor. Rock Mech. Rock Eng. 47, 303–311. https://doi.org/10.1007/s00603-013-0517-1
DOI: 10.5281/zenodo.6811594
title: Dataset for "Emerging tremors and increasing seismic noise precede micro-earthquakes triggered in a fluid-activated shale fault slip experiment (2015, Mt Terri URL, Switzerland)"

description: This dataset contains the raw data of the injection experiment, performed in the Mt Terri Underground Platform in 2015 and used in the article: <strong>De Barros, L., </strong>Guglielmi, Y., F. Cappa, C. Nussbaum, J. Birkholzer, 2023. Induced microseismicity and tremor signatures illuminate different slip behaviors in a natural shale fault reactivated by a fluid pressure stimulation (Mont Terri), <em>Geophysical Journal International</em>, 10.1093/gji/ggad231<br> <br> From a horizontal gallery, three vertical boreholes allowed the deployment of an injection probe (called SIMFIP; Guglielmi et al., 2014) and the monitoring sensors in the upper compartment of a N50°-60°SE fault zone. The 2.4 m long injection chamber of the SIMFIP probe was centered at 340.6 m depth, where a 3D displacement sensor was anchored on the borehole walls. A second SIMFIP probe is located 3.1 m northwest of the injection at a depth of 337.65 m, with another deformation sensor. Both deformation sensors measured the full strain tensor thanks to a Bragg optic fiber network, jointly with a fluid pressure sensor. A third borehole, located 2 m north of the monitoring probe, was dedicated to seismic monitoring. Two sets of collocated sensors, composed of a vertical geophone, a 3C accelerometer and an acoustic sensor, were positioned 9 m apart, above and below the main fault zone. These seismic sensors have a flat response in the ranges 0.01-0.5 kHz, 0.01-4 kHz and 0.5-10 kHz, respectively. Finally, the flowrate and pressure were also measured at the injection pump, located in the gallery.<br> For more details on the injection, we refer the reader to:<br> • Jeanne, P., Guglielmi, Y., Rutqvist, J., Nussbaum, C., Birkholzer, J., 2018. Permeability Variations Associated With Fault Reactivation in a Claystone Formation Investigated by Field Experiments and Numerical Simulations. J. Geophys. Res. Solid Earth 123, 1694–1710. https://doi.org/10.1002/2017JB015149<br> • Guglielmi, Y., Nussbaum, C., Cappa, F., De Barros, L., Rutqvist, J., Birkholzer, J., 2021. Field-scale fault reactivation experiments by fluid injection highlight aseismic leakage in caprock analogs: Implications for CO2 sequestration. Int. J. Greenh. Gas Control 111, 103471. https://doi.org/10.1016/j.ijggc.2021.103471<br> • Guglielmi, Y., Nussbaum, C., Jeanne, P., Rutqvist, J., Cappa, F., Birkholzer, J., 2020. Complexity of Fault Rupture and Fluid Leakage in Shale: Insights From a Controlled Fault Activation Experiment. J.Geophys. Res. Solid Earth 125, e2019JB017781. https://doi.org/10.1029/2019JB017781<br> • Guglielmi, Y., Cappa, F., Lançon, H., Janowczyk, J.B., Rutqvist, J., Tsang, C.F., Wang, J.S.Y., 2014. ISRM Suggested Method for Step-Rate Injection Method for Fracture In-Situ Properties (SIMFIP): Using a 3-Components Borehole Deformation Sensor. Rock Mech. Rock Eng. 47, 303–311. https://doi.org/10.1007/s00603-013-0517-1
DOI: 10.6084/m9.figshare.16419132.v3
title: Supplementary Data_Perceptions of Climate Change Kenya

description: Supplementary data for the manuscript entitled 'Perceptions of Ecosystem Services and Climate Change in the forests of Mount Elgon and Mount Kenya, Kenya'<br>
DOI: 10.6084/m9.figshare.12649550.v1
title: cleaned datasets created for MScThesis research

description: The cleaned datasets used for analyses within my MSc thesis entitled: "From land to sea: an assessment of global fungal diversity and description"<br>These are edited data originally sourced from public databases IndexFungorum, WoRMS and Mycobank.<br>Original data from each was downloaded in September 2019 and January 2020.
DOI: 10.6084/m9.figshare.23452709.v1
title: V133I_Pathogenic

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23452829
title: D77V_Benign

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23452829.v1
title: D77V_Benign

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23454701.v1
title: D79V_Benign

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23449682
title: L108P_Pathogenic

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23450348
title: S170R_Pathogenic

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23450348.v1
title: S170R_Pathogenic

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23452709
title: V133I_Pathogenic

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23454701
title: D79V_Benign

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23443967
title: G165R_Pathogenic

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23449682.v1
title: L108P_Pathogenic

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23450540
title: Y27S_Pathogenic

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23450540.v1
title: Y27S_Pathogenic

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23452808
title: P246L_Pathogenic

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23452808.v1
title: P246L_Pathogenic

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23442890.v1
title: T202I_Pathogenic

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23443964
title: G165V_Pathogenic

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23443964.v1
title: G165V_Pathogenic

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23442659
title: V175M_Pathogenic

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23442752
title: Y155C_Pathogenic

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23442890
title: T202I_Pathogenic

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23443562.v1
title: F90S_Pathogenic

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23443967.v1
title: G165R_Pathogenic

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23442221
title: G129E_Pathogenic

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23442659.v1
title: V175M_Pathogenic

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23442752.v1
title: Y155C_Pathogenic

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23443562
title: F90S_Pathogenic

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23441720
title: C136R_Pathogenic

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23441942
title: G129R_pathogenic

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23442320
title: H123D_Pathogenic

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23442320.v1
title: H123D_Pathogenic

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23442413
title: I101T_Pathogenic

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23442413.v1
title: I101T_Pathogenic

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23441261
title: R130P_Pathogenic

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23441261.v1
title: R130P_Pathogenic

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23441270.v1
title: C124R_Pathogenic

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23441942.v1
title: G129R_pathogenic

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23442221.v1
title: G129E_Pathogenic

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23309333.v1
title: H93R_Pathogenic

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23309438
title: R130G_Pathogenic

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23309438.v1
title: R130G_Pathogenic

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23441270
title: C124R_Pathogenic

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23309333
title: H93R_Pathogenic

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23441720.v1
title: C136R_Pathogenic

description: A total of 213 PTEN missense variants comprising of 147 VUS, 54 ClinVar Pathogenic and 12 Benign variants were slected for classification utilizing the MD simulations study.
DOI: 10.6084/m9.figshare.23498252.v1
title: <strong>Distribution of N and recently fixed C among a common mycorrhizal network linking an invasive plant, </strong><em><strong>Solidago canadensis</strong></em><strong>, and a native plant, </strong><em><strong>Kummerowia striata</strong></em>

description: original data for paper titled " <strong>Distribution of N and recently fixed C among a common mycorrhizal network linking an invasive plant, </strong><em><strong>Solidago canadensis</strong></em><strong>, and a native plant, </strong><em><strong>Kummerowia striata</strong></em>".
DOI: 10.15151/esrf-es-1178037339
title: Temperature evolution of crystallographic parameters in pyrochlore iridates

description: The rare-earth iridates, with general formula A2Ir2O7 (A = rare-earth ion), crystallise in a pyrochlore structure. They display a wide range of exotic magnetic and electronic low temperature properties, dependent on the specific rare-earth ion, due to the inherent magnetic geometric frustration of the rare-earth and Ir4+ (J = ½) corner-sharing tetrahedral lattices. Surprisingly however, initial magnetic ordering of the Ir4+ sublattice appears to be primarily dependent on the A ion size, rather than the rare-earth’s specific electronic properties. To investigate this relationship, we have measured the lattice compressibility and the structural evolution with pressure of several A2Ir2O7 members at room temperature and 20 K (ESRF, ID15b, HC-4856). To corroborate our very recent results, the temperature evolution of structural parameters is proposed to be investigated. We request beam time to use synchrotron powder X-ray diffraction and ID22 diffractometer.
DOI: 10.34740/kaggle/dsv/5897792
title: Elon Musk Tweets
DOI: 10.6084/m9.figshare.22775696.v1
title: Upper Critical Solution Temperature Polymer Assemblies via Variable Temperature Liquid Phase Transmission Electron Microscopy and Liquid Resonant Soft X-Ray Scattering

description: Source data file containing MALDI-IMS data and X-ray scattering data.
DOI: 10.6084/m9.figshare.22775696
title: Upper Critical Solution Temperature Polymer Assemblies via Variable Temperature Liquid Phase Transmission Electron Microscopy and Liquid Resonant Soft X-Ray Scattering

description: Source data file containing MALDI-IMS data and X-ray scattering data.
DOI: 10.6084/m9.figshare.22874918.v1
title: Splicing activates transcription from weak promoters upstream of alternative exons

description: Source Data
DOI: 10.6084/m9.figshare.22874918
title: Splicing activates transcription from weak promoters upstream of alternative exons

description: Source Data
DOI: 10.6084/m9.figshare.22958399
title: GPR data and attribute analysis

description: GPR attribute analysis. This includes Slope of Reflection Strength (SRS),Slop of Instantaneous Frequency (SIF),Product of Instantaneous Amplitude and Cosine of Instantaneous Phase (PIACIP), and Arc length of Time Window (AL).
DOI: 10.5281/zenodo.8022111
title: Dataset for: Exploring Battery Cathode Materials in the Li-Ni-O Phase Diagrams using Structure Prediction

description: The Li-Ni-O phase diagram contains several electrochemically active ternary phases. Many compositions and structures in this phase space can easily be altered by (electro-)chemical processes, yielding many more (meta-)stable structures with interesting properties. In this study, we use<em> ab initio </em>random structure searching (AIRSS) to accelerate materials discovery of the Li-Ni-O phase space. We demonstrate that AIRSS can efficiently explore structures (e.g. LiNiO<sub>2</sub>) displaying dynamic Jahn-Teller effects. A thermodynamically stable Li<sub>2</sub>Ni<sub>2</sub>O<sub>3</sub> phase which reduces the thermodynamic stability window of LiNiO<sub>2</sub> was discovered. AIRSS also encountered many dynamically stable structures close to the convex hull. Therefore, we confirm the presence of metastable Li-Ni-O phases by revealing their structures and properties. This work will allow Li-Ni-O phases to be more easily identified in future experiments and help to combat the challenges in synthesizing Li-Ni-O phases. This dataset contains the raw research data and key analysis files for "Exploring Battery Cathode Materials in the Li-Ni-O Phase Diagrams using Structure Prediction". `known_phases_aiida_data.zip` and `new_phases_exports.aiida.zip` contain the archives exported from the AiiDA framework which was used to perform parts of the DFT calculations for this project. `search_data.zip` contains search seed files and the structures generated by the searches. `data_analysis.zip` contains the data and notebooks to reproduce the figures and tables shown in the manuscript.
description: Funding Acknowledgements: The Faraday Institution (FIRG017, FIRG030) HIGH END COMPUTING (HEC) Materials Chemistry Consortium (EP/L000202, EP/R029431, EP/T022213) UK Materials and Molecular Modelling Hub (EP/P020194/1, EP/T022213/1)