Find research datasets worth reusing

Supplementary material of Kristallbrockensalt microstructures of the Zechstein-2 rock salt from the Northern Netherlands

This dataset represents supporting data for the paper entitled  Grain size dependent large rheology contrasts of halite at low deviatoric stress: evidence from microstructural study of naturally deformed gneissic Zechstein-2 rock salt (Kristallbrockensalz) from the Northern Netherlands , which has been submitted to Solid Earth journal The so-called Kristallbrocken salt has a unique and quite spectacular grain size distribution and marks an important horizon throughout the entire Permian Basin. Based on microstructural observations we interpret that fine grained halite deformed much weaker (i.e. faster) and has characteristic pressure-solution structures, as compared to much less deformed Kristallbrocken megacrystals that developed subgrains during dislocation creep. This dataset contains (1) a collection of addiational micrographs from gamma irradiated halite thin sections, (2) high resolution sample overview scans in tramsmitted and reflected light microscopy and grain and subgrainsize measurements, (3) Supportind EBSD material, (4) BIB-SEM material and (5) XRD results of sedond phase inclusions. (1), (3) and (4) are compiled in PowerPoint (.pptx) format, (2) contains multiple files of microscopical images or panoramas overlain with interpreted digitized grain and subgrain boundaries compiled in Inkscape (.svg) files together with corresponding measurements in Excel (.xls) files and (5) a text document (.txt) of XRD second phase results.

Training data for 'Functional annotation of protein sequences' tutorial (Galaxy Training Material)

The data provided here are part of a Galaxy Training Network tutorial for functional annotation of protein sequences.

Galaxy Training Material - Peak detection with recetox-aplcms

This dataset contains the training data for the recetox-aplcms GTN Tutorial. It includes 3 LC-ESI+-HRMS DDA files containing a mixture of pesticides and 3 GC-EI+-HRMS files from seminal plasma samples.

opencc-by-4.0Mar 2022View details →

SSHOC Training Material Video 1 - What is the Ethnic and Migrant Minorities Survey Registry? - training video in English, French and Spanish

A generic video, like the one produced about the SSH Marketplace, clearly and succinctly introducing and presenting the EMM Survey Registry. Would serve as an introductory video to train any new user on what the EMM Survey Registry is and who this tool is for. Would also present all available resources about the Registry (so introducing the full set of training materials available about the Registry) This has been produced in English, French, Spanish

THE BATTERY MATERIALS SOURCING ENGINEER - Mark Huijben - University of Twente

The need for radically different usage of our planet's resources has never been so high. There is an increasing demand for electricity in the years to come. We need innovative ways to store that energy to take it out whenever and wherever we need it.  Professor in Nanomaterials for Energy Conversion and Storage, Mark Huijben elaborates on the development of next-level batteries. Not only having optimal performance but also being more sustainable. That begins with the design of a battery. Also, the materials being used make a huge difference in their recyclability.  Gerwin Hoogsteen, a researcher on energy management for smart grids, surprises us with a creative perspective on energy usage and storage. He takes us to the year 2030 where energy is stored locally, in self-driving cars that drive to places with energy overload and take it to the place where you need it: your home. What hurdles do we need to take to make this a reality?

Supporting material for "Impact of gender on the formation and outcome of formal mentoring relationships in the life sciences"

This repository contains data and analysis code associated with the manuscript: L.P. Schwartz, J. Liénard, S. V. David. (2022) "Impact of gender on formation and outcome of formal mentoring relationships in the life sciences." Figures and tables in the manuscript can be produced by running the make_figures.ipynb notebook. Figures have been marked with headings indicating their position in the manuscript (Figure 1, Figure S1, etc.). In addition, the notebook contains code to reproduce regression analyses that are cited in the text but not directly associated with a figure. Data on mentoring relationships derives from Academic Family Tree (AFT, www.academictree.org) and public data sources on funding, publications, and awards. Inclusion criteria, public data sources, and procedures for linking across sources are described in the manuscript.  Personal identifiers for researchers have been anonymized, but remain consistent across all data in the repository. In other words, the personal identifier "1" refers to the same person in all dataframes in the repository. But, that person is *not* the same researcher identified as "1" on the public AFT website. Installation Requires Python 3.x. and Pandas. To load required libraries using Anaconda, run: `conda create --name aft -c conda-forge pandas numpy scipy ipython jupyterlab scipy scikit-learn pandas matplotlib numpy statsmodels seaborn pytables` Dataframes Data is stored as a series of Pandas dataframes within HDF5 or CSV files: * cng_tc: The primary dataset used in the analysis. The name is an acronym for "connections" (i.e. training relationships, "cn"), "gender" ("g"), and "trainee count" ("tc"). Each row contains data on the mentor and trainee in one training relationship. See manuscript for inclusion criteria. * mentors: Data on mentors. Each row contains data on one mentor. See manunscript for inclusion criteria. * mentors_grants, mentors_hindex, mentors_locs_ranked: Subset of mentors with data available for funding (mentors_grants), citation (mentors_hindex), and institution rank (mentors_locs_ranked). * mentors_nobel, mentors_hhmi, mentors_nas: Subsets of mentors that received a Nobel (mentors_nobel), Howard Hughes Medical Institute grants (mentors_hhmi), or membership in the National Academy of Sciences (mentors_nas). See manuscript for details of data sources and linking procedures. * cn, cng, first_names, gn, gn_all, locs: Partial data (connections only, inferred gender only, connections and gender only, location only, first names and inferred gender only) for more inclusive sets of researchers in AFT. They are generally not used used for analysis, but have been included here to calculate statistics on the total amount of data included and to screen for data from U.S. locations. * nsf_gender_phds, nsf_gender_pds: National Science Foundation survey data on gender and fraction PhDs conferred per year (nsf_gender_phds) or fraction postdocs employed per year (nsf_gender_pds). See manuscript for details of data source. * photo: Data for validation of gender inference method. Dataframe columns * amount: Mentor's total funding * amount_adj: Mentor's total funding (adjusted to 2020 dollars) * broad_field: Mentor's general research area (e.g., life sciences, engineering, based on National Science Foundation classifications) * continue: Whether trainee went on to become a mentor (i.e., has trainees listed in AFT) * country: Country in which mentor's current institution is located * firstname: First name of researcher (table of first names is not aligned with tables containing anonymized personal identifiers) * first_grant_year: Year of mentor's first grant * funding_rate: Mentor's annual funding rate (since first grant) * funding_rate_adj: Mentor's annual funding rate (since first grant) adjusted to 2020 dollars * hhmi: Whether mentor was granted HHMI funding * hindex: Mentor's hindex * location: Name of mentor's current institution * locid: Identifier for mentor's institution * locid_rank: Postion of mentor's institution in 2015 Quacquarelli-Symonds rankings (lower numbers are better) * locid_rank_rev: Reversed version of "locid_rank" (i.e., higher numbers are better) * majorarea: Mentor's specific research area (e.g, neuroscience) * male_mentor, male trainee: Whether the probability that a researcher's first name is used by a person identifying as a man meets threshold (see manuscript for details on gender inference using first names) * match_score: Score for string match between institution or name of awardee and researcher * mentor_career_start: The date at which the mentor's academic career began * mentor_continue_rate: Fraction of mentor's trainees that become mentors * mentor_continue_rate_ft: Fraction of mentor's woman trainees that become mentors * mentor_continue_rate_mt: Fraction of mentor's man trainees that become mentors * mentor_t_p_male0: Fraction of mentor's trainees that are men * mentor_t_p_male0_gs: Fraction of mentor's trainees that are men (graduate students only) * mentor_t_p_male0_pd: Fraction of mentor's trainees that are men (postdocs only) * mentor_tcount0: Mentor's total number of trainees * nas: Whether mentor is a member of the National Academy of Sciences * nobel: Whether mentor is a Nobel laureate * p_male_mentor, p_male_trainee: Probability that a researcher's first name is used by a person identifying as a man * pid: Anonymized identifier of researcher * pid_mentor: Anonymized identifier of mentor in training relationship * pid_trainee: Anonymized identifier of trainee in training relationship * pq: "1" if data on training relationship is drawn from ProQuest database and has not been manually edited a human AFT user * relation: Type of training relationship (1: graduate student, 2: postdoc) * scorer1, scorer2, scorer3: Results of photo validation of gender inference for each scorer * start: Training start year * stop: Training end year * trainee_tcount: Total people that the trainee has trained * triad: Whether trainee has participated in both a graduate-level and postdoctoral training relationship The cn dataframe follows slightly different naming conventions, but is not generally used in the analysis (pid1 = pid_trainee, pid2 = pid_mentor, startdate = start, stopdate = stop).

Training data for 'Refining Manual Genome Annotations with Apollo (eukaryotes)' tutorial (Galaxy Training Material)

The data provided here are part of a Galaxy Training Network tutorial for manual curation of eukaryotic genome annotation using Apollo.

Supplementary Material for Disruptive Solutions on Requirement Engineering for Agile Software Development: A tertiary study

This repository delivers the supplementary material for the paper: Disruptive Solutions on Requirement Engineering for Agile Software Development: A tertiary study. In the following, we present the abstract of the study: Context: Agile Software Development (ASD) is a disruptive process compared to traditional software development. Therefore, traditional Requirements Engineering (RE) forms may not be the best way to do RE for ASD (RE-ASD). Objective: Working with ASD using traditional RE ways could limit ASD's potential. Thus, it is necessary to investigate what academia and industry have done in RE to take full advantage of all of the capabilities of ASD beyond traditional RE. Method: We conducted a Tertiary Study looking for solutions for RE-ASD using the Systematic Literature Review (SLR) protocol described by Kitchenham and Charters. We then categorized the solutions into families using Targeted Coding and Constant Comparison, tools from Socio-Technical Grounded Theory (STGT). Afterward, we classified the solutions as disruptive using our model based on the Hype Level Curve concept, assessing their hype (popularity) in the software engineering community using Google Trends and Google Colab tools. Results: After executing the SLR protocol, we accepted 37 studies and encountered 136 solutions used by academia and industry for RE-ASD. We categorized these solutions into 21 solution families, six of which we classified as disruptive. Design Thinking (DT) and Artificial Intelligence (AI) were the two families of solutions that stood out the most. We also identified the type of solution (e.g., process, method, technique, tool, model, framework) and domain (academia or industry). Furthermore, we cataloged the challenges presented by the solutions. Conclusion: We concluded that only a few solutions that have been used for RE-ASD have the power to successfully challenge the mainstream Agile Software Development process by using innovation (26 out of 106). There is a gap between academia and industry regarding these disruptive solutions, and some challenges still need to be addressed in using these solutions. The repository contains the following: <ul> <li>Dataset from the Tertiary Study: <ul> <li>Data of the retrieved studies. It presents the classifications of the documents as 'Accepted,' 'Rejected' (with the indication of the step of the protocol the authors rejected the study), or 'Duplicated.'</li> <li>Data of all solutions retrieved from the accepted studies</li> </ul> </li> <li>Socio-Technical Grounded Theory (STGT) tools <ul> <li>Result of the use of Targeted Coding and Constant Comparison</li> </ul> </li> <li>The Google Colab Notebook <ul> <li>Code in python</li> <li>Results</li> </ul> </li> </ul>

Training material for Genome assembly quality control (Galaxy Training Network tutorial)

This Zenodo repository includes the required datasets for following the GTN: Genome assembly quality control.

Mining minerals and critical raw materials from bittern: Understanding metal ions fate in saltwork ponds

Seawater represents a potential resource for raw materials extraction. Although NaCl is the most representative mineral extracted other valuable compounds such as Mg, Li, Sr, Rb and B and elements at trace level (Cs, Co, In, Sc, Ga and Ge) are also contained in this “liquid mine”. Most of them are considered as Critical Raw Materials by the European Union. Solar saltworks, providing concentration factors of up-to 20 to 40, offer a perfect platform for the development of minerals and metal recovery schemes taking benefit of the concentration and purification achieved along the evaporation saltwork ponds. However, the geochemistry of these elements in this environment has not been yet thoroughly evaluated. Their knowledge could enable the deployment of technologies capable to achieve the recovery of valuable minerals. The high ionic strengths expected (0.5–7 mol/kg) and the chemical complexity of the solutions imply that only numerical geochemical codes, as PHREEQC, and the use of Pitzer model to estimate the activity coefficients of the different species in solution can be adopted to provide valuable description of the systems. In the present work, for the first time, PHREEQC Pitzer code database was extended to include the target minor and trace elements using Trapani saltworks (Sicily, Italy) as a case study system. The model was able to predict: i) the purity in halite and the major impurities contained, mainly Ca,Mgand sulphate species; ii) the fate of minor components as B, Sr, Cs, Co, Ge and Ga along the evaporation ponds. The results obtained pose a fundamental step in critical raw materials mining from seawater brine, for process intensification and combination with desalination.

opencc-by-4.0Jun 2020View details →

Manual material handling in the supermarket sector: full dataset

Manual material handling in the supermarket sector: full dataset Sup. figures 1-50: Trunk flexion/extension (T8 relative to pelvis), lateral bending and rotation, knee flexion/extension and shoulder flexion/extension joint angles over the complete lifting cycles for all 50 analyzed manual material handling tasks (listed in Tables 1a and 1b). Sup. figures 51-76: Knee and shoulder (glenohumeral) resultant joint reaction forces, as well as L4-L5 and L5-S1 axial compression, anteroposterior shear and mediolateral shear forces over the complete lifting cycles for the 26 manual material handling tasks included in the musculoskeletal model analysis (listed in Table 9) . Tables 1-8 (a and b): Peak, 90th and 50th percentile muscle activity of trapezius descendens and erector spinae longissimus, bilateral peak joint angle and ROM for knee flexion-extension and shoulder flexion-extension for the 50 manual material handling tasks (description in Table 1a and 1b). The tasks are ranked from 1 to 50 (highest to lowest) for each outcome variable.  Tables 9-12: L5-S1 axial compression peak force and impulse, anteroposterior shear and mediolateral shear peak forces (Table 10), bilateral knee and shoulder (glenohumeral) peak resultant joint reaction forces (Table 11) as well as bilateral peak net total shoulder joint and knee flexion/extension moment (Table 12) for the 26 manual material handling tasks included in the musculoskeletal model analysis (description in Table 9). The tasks are ranked from 1 to 26 (highest to lowest) for each variable.

Characterization of the material behavior and identification of effective elastic moduli based on molecular dynamics simulations of coarse-grained silica: dataset

Abstract: (from [1]) <blockquote> The addition of fillers can significantly improve the mechanical behavior of polymers. The responsible mechanisms at the molecular level can be well assessed by particle-based simulation techniques, such as molecular dynamics. However, the high computational cost of these simulations prevents the study of macroscopic samples. Continuum-based approaches, particularly micromechanics, offer a more efficient alternative but require precise constitutive models for all constituents, which are usually unavailable at these small length scales. In this contribution, we derive a molecular-dynamics-informed constitutive law by employing a characterization strategy introduced in a previous publication. We choose silicon dioxide (silica) as an exemplary filler material used in polymer composites and perform uniaxial and shear deformation tests with molecular dynamics. The material exhibits elastoplastic behavior with a pronounced anisotropy. Based on the pseudo-experimental data, we calibrate an anisotropic elastic constitutive law and reproduce the material response for small strains accurately.   The study validates the characterization strategy that facilitates the calibration of constitutive laws from molecular dynamics simulations. Furthermore, the obtained material model for coarse-grained silica forms the basis for future continuum-based investigations of polymer nanocomposites. In general, the presented transition from a fine-scale particle model to a coarse and  computationally efficient continuum description adds to the body of knowledge of molecular science as well as the engineering community.   </blockquote> Contact: Maximilian Ries Institute of Applied Mechanics Friedrich-Alexander-Universität Erlangen-Nürnberg Egerlandstr. 5 91058 Erlangen Software: All simulations were performed with LAMMPS [3], version: 29 Oct 2020 / 20201029 Compiled with Compiler: GNU C++ 4.8.5 20150623 (Red Hat 4.8.5-39) with OpenMP not enabled C++ standard: C++11 Active compile time flags: -DLAMMPS_GZIP -DLAMMPS_SMALLBIG Installed packages: CLASS2, KSPACE, MANYBODY, MC, MOLECULE, MPIIO, OPT, VORONOI, USER-INTEL, USER-MISC, USER-MOLFILE, USER-NETCD License: Creative Commons Attribution 4.0 International   Context: Data set supplementing  journal paper: [1] Ries, M.; Bauer, C.; Weber, F.; Steinmann, P. & Pfaller, S., "Characterization of the material behavior and identification of effective elastic moduli based on molecular dynamics simulations of coarse-grained silica", Mathematics and Mechanics of Solids, 2022, 108128652211080. This dataset contains the results presented in [1] and the necessary data to obtain those. Content: The files to reproduce our simulations and their results are structured as follows: <ul> <li>01_potentials tabulated potentials calibrated via iterative Boltzmann inversion in [2] kindly provided by the Müller-Plathe group at Technische Universität Darmstadt <ul> <li>Angle_table angular interactions</li> <li>Bond_table bond interactions</li> <li>Nonbond_table pair interactions</li> </ul> </li> <li>02_sample Lammps data file (molecular style) of the investigated silica sample</li> <li>03_simulations The condensed simulation directories with the naming convention given below are organized in the following subfolders: <ul> <li>01_time-proportional time-proportional simulation data</li> <li>02_time-periodic time-periodic simulation data</li> </ul> </li> </ul> Each simulation directory contains: <ul> <li>lammps input file (*.in) of the specific simulation</li> <li>input.prm: input parameters of the specific simulation (read by the input file)</li> <li>meta.info: meta data of the specific simulation run</li> <li>LAMMPS_out: simulation results (lammps thermo_out) in tabulated form, an overview of columns is given below <ul> <li>thermo_out.Dat: raw output</li> <li>thermo_out_SG.Dat: smoothed output (Savitzky-Golay filter)</li> <li>thermo_out_STD.Dat: standard deviation of raw output</li> </ul> </li> </ul> Naming convention: Silica-[deformation]-[direction]_[deformation function]-[deformation magnitude]_[deformation rate] ●    [deformation]: uniaxial tension (UT), simple shear (SS) ●    [direction]: deformation carried out in X/Y/Z (UT) or XY/XZ/YZ (SS) ●    [deformation function]: time-proportional (strain), time-periodic (strain_ampl) ●    [deformation magnitude]: maximum strain (time-proportional), strain amplitude (time-periodic); unitless ●    [deformation rate]: rate-[strain rate] (only time-proportional): 0.001/ns-0.1/ns Output quantities (columns of *.Dat files): ●    Step: time step ●    Time: time in fs ●    TotEng: total energy in kcal/mol ●    PotEng: potential energy in kcal/mol ●    KinEng: kinetic energy in kcal/mol ●    E_pair: pair energy in kcal/mol ●    E_bond: bond energy in kcal/mol ●    E_angle: angle energy in kcal/mol ●    E_dihed: dihedral energy in kcal/mol ●    Temp: temperature in K ●    Press: hydrostatic pressure in atm ●    Pxx: xx component of pressure tensor in atm ●    Pyy: yy component of pressure tensor in atm ●    Pzz: zz component of pressure tensor in atm ●    Pxy: xy component of pressure tensor in atm ●    Pxz: xz component of pressure tensor in atm ●    Pyz: yz component of pressure tensor in atm ●    Volume: volume of simulation box in (Angstroms)^3 ●    Lx: box length in x direction in Angstroms ●    Ly: box length in y direction in Angstroms ●    Lz: box length in z direction in Angstroms ●    Density: density in g/(cm^3) ●    c_RG: radius of gyration in Angstroms ●    c_RG[1]: squared radius of gyration tensor (xx component) in (Angstroms)^2 ●    c_RG[2]: squared radius of gyration tensor (yy component) in (Angstroms)^2 ●    c_RG[3]: squared radius of gyration tensor (zz component) in (Angstroms)^2 ●    c_RG[4]: squared radius of gyration tensor (xy component) in (Angstroms)^2 ●    c_RG[5]: squared radius of gyration tensor (xz component) in (Angstroms)^2 ●    c_RG[6]: squared radius of gyration tensor (yz component) in (Angstroms)^2 ●    c_bondave[1]: bond energy averaged over all atoms in kcal/mol ●    c_bondave[2]: bond distance averaged over all atoms in  Angstroms ●    c_bondave[3]: squared bond distance averaged over all atoms in (Angstroms)^2 ●    c_angleave[1]: angle energy averaged over all atoms in kcal/mol ●    c_angleave[2]: angle averaged over all atoms degree ●    c_angleave[3]: cosine of angle (unitless) ●    c_angleave[4]: squared cosine of angle (unitless) ●    c_MSD[1]: mean squared displacement x-direction in (Angstroms)^2 ●    c_MSD[2]: mean squared displacement y-direction in (Angstroms)^2 ●    c_MSD[3]: mean squared displacement z-direction in (Angstroms)^2 ●    c_MSD[4]: total mean squared displacement in (Angstroms)^2 ●    c_COM[1]: x coordinate of center of mass in Angstroms ●    c_COM[2]: y coordinate of center of mass in Angstroms ●    c_COM[3]: z coordinate of center of mass in Angstroms ●    v_strain_xx: xx component of engineering strain tensor (unitless)   ●    v_strain_yy: yy component of engineering strain tensor (unitless)    ●    v_strain_zz: zz component of engineering strain tensor (unitless)    ●    v_vMisesequivstress: von Mises equivalent stress in MPa ●    v_Cauchy_xx: xx component of stress tensor in MPa   ●    v_Cauchy_yy: yy component of stress tensor in MPa ●    v_Cauchy_zz: zz component of stress tensor in MPa ●    v_Cauchy_xy: xy component of stress tensor in MPa ●    v_Cauchy_xz: xz component of stress tensor in MPa ●    v_Cauchy_yz: yz component of stress tensor in MPa ●    v_strain_xy: xy component of engineering strain tensor (unitless)   ●    v_strain_xz: xz component of engineering strain tensor (unitless)   ●    v_strain_yz: yz component of engineering strain tensor (unitless)   References: [1] Ries, M.; Bauer, C.; Weber, F.; Steinmann, P. & Pfaller, S., "Characterization of the material behavior and identification of effective elastic moduli based on molecular dynamics simulations of coarse-grained silica", Mathematics and Mechanics of Solids, 2022, 108128652211080. [2] Ghanbari, A.; Ndoro, T. V. M.; Leroy, F.; Rahimi, M.; Böhm, M. C. & Müller-Plathe, F., “Interphase Structure in Silica-Polystyrene Nanocomposites: A Coarse-Grained Molecular Dynamics Study”, Macromolecules, 2012, 45, 572-584. [3] Plimpton, S., “Fast parallel algorithms for short-range molecular dynamics,” Journal of computational physics, 1995, 117, 1-19.

Replication material for "Competition with Indivisibilities and Few Traders"

The replication material for the paper Competition with Indivisibilities and Few Traders.

opencc-zeroAug 2022View details →

dryad40/100

Supplementary material and supplementary data files for: Handling logical character dependency in phylogenetic inference: Extensive performance testing of assumptions and solutions using simulated and empirical data

Logical character dependency is a major conceptual and methodological problem in phylogenetic inference of morphological datasets, as it violates the assumption of character independence that is common to all phylogenetic methods. It is more frequently observed in higher-level phylogenies or in datasets characterizing major evolutionary transitions, as these represent parts of the tree of life where (primary) anatomical characters either originate or disappear entirely. As a result, secondary traits related to these primary characters become "inapplicable" across all sampled taxa in which that character is absent. Various solutions have been explored over the last three decades to handle character dependency, such as alternative character coding schemes and, more recently, new algorithmic implementations. However, the accuracy of the proposed solutions, or the impact of character dependency across distinct optimality criteria, has never been directly tested using standard performance measures. Here, we utilize simple and complex simulated morphological datasets analyzed under different maximum parsimony optimization procedures and Bayesian inference to test the accuracy of various coding and algorithmic solutions to character dependency. This is complemented by empirical analyses using a recoded dataset on palaeognathid birds. We find that in small, simulated datasets, absent coding performs better than other popular coding strategies available (contingent and multistate), whereas in more complex simulations (larger datasets controlled for different tree structure and character distribution models) contingent coding is favored more frequently. Under contingent coding, a recently proposed weighting algorithm produces the most accurate results for maximum parsimony. However, Bayesian inference outperforms all parsimony-based solutions to handle character dependency due to fundamental differences in their optimization procedures—a simple alternative that has been long overlooked. Yet, we show that the more primary characters bearing secondary (dependent) traits there are in a dataset, the harder it is to estimate the true phylogenetic tree, regardless of the optimality criterion, owing to a considerable expansion of the tree parameter space.

Supplementary Material for Design of Frustrated Lewis Pair Catalysts for Direct Hydrogenation of CO2

Supplementary Material for "Design of Frustrated Lewis Pair Catalysts for Direct Hydrogenation of CO2"

Bulk and critical material demand for selected 'Starter Kit' energy system models - dataset

This repository contains the data related to the Data in Brief article titled: Bulk and critical material demand for selected ‘Starter Kit’ energy system models. The data include the modeled mass of materials and their embodied emissions. A metadata file is also included to clarify the units, materials and scenario names.

The Dataset of Quantifying Alignment Deviations for Uniaxial Material Mechanical Testing via Automated Machine Learning

The dataset consists of 4 alignment deviations of the uniaxial testing machine as well as 12 strain measurement points on cruciform specimens. A deep learning model is trained on the dataset to quantify 4 alignment deviations using 12 strain values on a thin plate specimen. The design of experiments includes Optimal Latin Hypercube, numerical modelling of Finite Element Methods. Using the Optimal Latin Hypercube, 12496 distinct groups of DOE simulation tests are constructed. Under the boundary conditions of 4 distinct deviations, 12 strain values at the required location on the cruciform specimen are obtained using Python scripts. The nine CSV files correspond to the nine analysis steps. The only difference among the nine analysis steps is the pretension force acting on RP1. Each CSV file contains 24 columns of data, and the corresponding contents of each column of data are as follows: <ul> <li>Columns 1-6 are the freedoms of RP1 reference point, which are U1, U2, U3, ur1, UR2 and UR3 respectively;</li> <li>Columns 7-12 are the freedoms of RP2 reference points, which are U1, U2, U3, ur1, UR2 and UR3 respectively;</li> <li>Columns 13-24 are the strain values of the last 12 strain measurements of the thin plate rectangular specimen。</li> </ul>

Supplementary material S2. Leiestes tomaszewskae sp. nov., holotype, Nr. 6723 [MAIG], X-ray micro-CT volume rendering of the habitus (without legs).

X-ray micro-CT volume rendering of the habitus (without legs) of Leiestes tomaszewskae sp. nov., holotype, Nr. 6723 [MAIG].

Supplementary material S1. Leiestes tomaszewskae sp. nov., holotype, Nr. 6723 [MAIG], X-ray micro-CT volume rendering of the habitus.

X-ray micro-CT volume rendering of the habitus of Leiestes tomaszewskae sp. nov., holotype, Nr. 6723 [MAIG].

Supplementary material S3. Leiestes tomaszewskae sp. nov., holotype, Nr. 6723 [MAIG], X-ray micro-CT volume rendering of the right antenna.

X-ray micro-CT volume rendering of the right antenna of Leiestes tomaszewskae sp. nov., holotype, Nr. 6723 [MAIG].