Find research datasets worth reusing

opencc-by-4.0Jul 2021View details →

Supplementary Materials for 'Mobile device-based Bluetooth Low Energy Database for range estimation in collaborative positioning'

Supplementary Materials for 'Mobile device-based Bluetooth Low Energy Database for range estimation in collaborative positioning' This package contains a collaborative BLE-RSS database, which considers different collaborative set-ups involving five mobile devices. The database is composed of three main subdirectories (Raw-Data, Processed-Data, and Code). The Code directory contains the Matlab script files used to process the raw data, visualize the processed data, and usage examples of the data provided. Further information is provided in the Readme file.

2D Sound Navigation - Tutorial Materials

Materials presented to the experiment participants to familiarize them with the navigation controls and auditory guidance.

Data of paper Chemically vapor deposited Eu3+:Y2O3 thin films as a material platform for quantum technologies

Data of the figures of the paper: N. Harada, A. Ferrier, D. Serrano, M. Persechino, E. Briand, R. Bachelet, I. Vickridge, J.-J. Ganem, P. Goldner, and A. Tallaire, Chemically Vapor Deposited Eu 3+:Y 2O 3thin Films as a Material Platform for Quantum Technologies, J. Appl. Phys. 128, 055304 (2020). doi: <a href="https://doi.org/10.1063/5.0010833">10.1063/5.0010833</a>

Electron Accepting Capacities of a wide variety of peat materials from around the Globe similarly explain CO2 and CH4 production

In peat soils the availability of terminal electron acceptors (TEAs), both inorganic and organic, largely determines the ratio of carbon dioxide to methane formation under waterlogged, anoxic conditions. The redox properties of peat organic matter and their relationship with anoxic carbon mineralization are yet only investigated for a limited amount of peat and reference materials, although electron accepting capacities of organic matter (EACOM) largely predominate over canonical inorganic TEAs in peatlands. To address this knowledge gap, we incubated 60 peat samples from four different depths of 15 sites located in five major peatland regions (including Germany, Sweden, Russia, France and Chile) distributed around the globe covering a variety of both bog and fen type samples and characterized their capacities to serve as electron acceptors for anaerobic carbon dioxide production. The dataset consists of a wide variety of recorded and calculated variables for a 56-day incubation of those samples. These variables include the formation and rates of methane, carbon dioxide, electron acceptor capacities and electron donator capacities at two different times, data on stable isotopes in delta notation (such as nitrogen, carbon and sulfur), molar element ratios for carbon/nitrogen, carbon/sulfur and nitrogen/phosphorus and elemental contents like silicon, phosphorus, sulfur, calcium and iron as well as specific fourier transformed infrared spectroscopy ratios regarding the ratios of polysaccharides and aromatic structures. The dataset was created mostly in 2019, with some additional measurements carried out in 2020 and 2021.

Supplementary Material of "How Does Author Affiliation Affect Preprint Citation Count? Analyzing Citation Bias at the Institution and Country Level"

The source code and dataset for the following paper: Nishioka, C., Färber, M., and Saier, T. How Does Author Affiliation Affect Preprint Citation Count? Analyzing Citation Bias at the Institution and Country Level. In Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2022 (JCDL '22), 2022.

Accompanying material for Article: No cash, no food. Gendered reorganization of livelihoods and food security in Cambodia

Results of the two questionnaire-base surveys carried on in 2016 and 2019 on rural livelihoods in Cambodia

opencc-zeroMay 2022View details →

dryad40/100

Dataset, statistical analysis code, and supplementary material of juvenile ravens' responses towards acoustic cues of different social categories

Social competence i.e., defined as the ability to adjust the expression of social behaviour to the available social information, is known to be influenced by early-life conditions. Brood size might be one of the factors determining such early conditions, particularly in species with extended parental care. We here tested in ravens, whether growing up in families of different sizes affects the chicks' responsiveness to social information. We experimentally manipulated the brood size of 20 captive raven families, creating either small or large families. Simulating dispersal, juveniles were separated from their parents and temporarily housed in one of two captive non-breeder groups. After five weeks of socialization, each raven was individually tested in a playback setting with food-associated calls from three social categories: sibling, familiar unrelated raven they were housed with, and unfamiliar unrelated raven from the other non-breeder aviary. We found that individuals reared in small families were more attentive than birds from large families, in particular towards the familiar unrelated peer. These results indicate that variation in family size during upbringing can affect how juvenile ravens value social information. Whether the observed attention patterns translate into behavioural preferences under daily life conditions remains to be tested in future studies.

dryad40/100

Online electronic material for: Macroevolutionary dynamics of climatic niche space

How and why lineages evolve along niche space as they diversify and adapt to different environments is fundamental to evolution. Progress has been hampered by the difficulties of linking a robust empirical characterization of species niches with flexible evolutionary models that describe their evolution. Consequently, the relative influence of abiotic and biotic factors remains poorly understood. Here we characterize species' two-dimensional temperature and precipitation niche space occupied (i.e., species niche envelope) as complex geometries and assess their evolution across all Aves using a model that captures heterogeneous evolutionary rates on time-calibrated phylogenies. We find that extant birds coevolved from warm, mesic climatic niches into colder and drier environments and responded to the K-Pg boundary with a dramatic increase in disparity. Contrary to expectations of subsiding rates of niche evolution, our results show that overall rates have increased steadily, with some lineages experiencing exceptionally high evolutionary rates, associated with colonization of novel niche spaces, and others showing niche stasis. Both competition- and environmental change-driven niche evolution transpire and result in highly heterogeneous rates near the present. Our findings highlight the growing ecological and conservation insights arising from model-based integration of comprehensive environmental and phylogenetic information.

opencc-zeroMay 2022View details →

Use of waste materials for 3D printing of cement-based materials

Experimental results: PSD = Particle Size Distribution XRF = X-Ray Fluorescence - chemical composition

Mixoplankton spatio-temporal diversity and its environmental drivers in the North Sea [Supplementary Material]

Supplementary Material of the PhD [Mixoplankton spatio-temporal diversity and its environmental drivers in the North Sea [Supplementary Material]

opencc-by-4.0Dec 2021View details →

Equivariant analytical mapping of first principles Hamiltonians to accurate and transferable materials models

Supporting data for <a href="https://arxiv.org/abs/2111.13736">https://arxiv.org/abs/2111.13736</a>. ACEhamiltonians.jl code This is an archived copy of the ACEhamiltonians.jl code to accompany the paper <a href="https://arxiv.org/abs/2111.13736">arXiv:2111.13736</a>. See <a href="https://github.com/ACEsuit/ACEhamiltoniansExamples">https://github.com/ACEsuit/ACEhamiltoniansExamples</a> for examples of how to use this code. The code is written in <a href="https://julialang.org/">Julia</a> and requires v1.6 or later. To install the Julia depenendencies: <pre><code><code>$ cd ACEhamiltonians.jl $ julia julia> import Pkg julia> Pkg.activate(".") julia> Pkg.instantiate() </code></code></pre> The scripts <code>test/plots.jl</code>, <code>test/fcc-to-bcc.jl</code> and <code>test/vacancy.jl</code> which produce all the plots in the paper can then run as, e.g. <pre><code><code>julia --project=. test/plots.jl </code></code></pre> Training data The <code>training_data</code> folder contains the atomic structure, Hamiltonian and overlap matrices stored in HDF5 format with the following schema: <ul> <li>Data Group : aitb/</li> <li>Datasets : <ul> <li>H : Real-space Hamiltonian Matrix. Type: Float64. Shape: Tensor(# of TB Cells, # of Rows, # of Columns)</li> <li>S : Real-space Overlap Matrix. Type: Float64. Shape: Tensor(# of TB Cells, # of Rows, # of Columns)</li> <li>energy : Energy. Unit: eV. Type: Float64. Shape: Scalar</li> <li>freeenergy : Free Energy. Unit: eV. Shape: Scalar</li> <li>unitcell : Unit cell vectors. Type: Float64. Shape: Matrix(3,3)</li> <li>positions : Atom positions. Type: Float64. Shape: Array(3)</li> <li>forces : (Optional, if available) Forces. Type: Float64. Shape: Array(3)</li> <li>metadata : JSON String including dictionary of information of FHIaims calculation (k-points, basis sets), TB Cells, Cutoff, Orbital definitions.,</li> </ul> </li> </ul> The molecular dynamics and FHI-aims parameters are described in the manuscript. On-site models The <code>onsite_models_ord2</code> folder contains our correlation order 2 models for the on site blocks of the Hamiltonian, in a JSON format readable by the <a href="https://github.com/acesuit/ACE.jl">ACE.jl</a> and <a href="https://github.com/ACEsuit/ACEhamiltonians.jl">ACEhamiltonians.jl</a> Julia packages. There are separate files for the Hamiltonian (<code>*_H.json</code>) and overlap (<code>*_S.json</code>) models. The JSON files also contain training and test sets and associated errors as plotted in Figure 3 in our manuscript. Models have a unique identifier (UUID) which is a hash of the input parameters and training data. The mapping from (order, max_degree) to UUID is as follows: <pre><code><code>(2,4) - 13427527590286463256 (2,5) - 10538156191357510769 (2,6) - 1646489440533135164 (2,7) - 12130775482127724115 (2,8) - 12487060958610974041 (2,9) - 2653067664384673997 (2,10) - 1143382251563115664 (2,11) - 4564001820340015372 (2,12) - 9474261500251782658 </code></code></pre> Off-site models The <code>offsite_models_ord1</code> and <code>offsite_models_ord2</code> folders contain our order 1 and order 2 offsite models for Hamiltonian and overlap matrices. The mapping from (H_order, H_max_degree) + (S_order, S_max_degree) to UUID is as follows: <pre><code><code>(1,6) + (1,8) - 7014526518680934587 (1,7) + (1,9) - 8594416159488562244 (1,8) + (1,10) - 10204186688118368371 (1,9) + (1,11) - 13078304848585360574 (1,10)+ (1,12) - 14750835312950641338 (1,11)+ (1,13) - 9883802224093245794 (1,12)+ (1,14) - 3907899412408606585 (1,13)+ (1,15) - 201683837542179657 (1,14)+ (1,16) - 277744202775070779 (2,6) + (1,8) - 4699475053563592071 (2,7) + (1,9) - 489637409713831432 (2,8) + (1,10) - 18034631670613263469 (2,9) + (1,11) - 720654516759450160 (2,10)+ (1,12) - 15214900801060024044 (2,11)+ (1,13) - 13798832597295943078 (2,12)+ (1,14) - 13162803789413134473 </code></code></pre> FCC only Onsite models: <pre><code><code>2 6 5311732756869418284 2 7 13030014632886405308 2 8 5820099621734447846 2 9 10161014511878227635 2 10 11298425190201843107 2 11 9932031839231628354 2 12 9447261873515969583 </code></code></pre> Optimised FCC model <code>16110190062237887798</code> BCC only Onsite models: <pre><code><code>2 6 8949023800586845770 2 7 8045797268444730200 2 8 6919809282139600809 2 9 9935027806122780319 2 10 6376963380608532713 2 11 5001375576268070883 2 12 9678585765722197901 </code></code></pre> Optimised BCC model <code>10293566074413000591</code> FCC+BCC optimised models Onsite models <pre><code><code>2 6 2154760103892646619 2 7 6450474921309693835 2 8 14227277988574899288 2 9 476820595195218567 2 10 5364136683220082110 2 11 14619519825012606580 2 12 14181614899005838824 </code></code></pre> Offsite FCC+BCC optimised model - <code>4570230078043807257</code> Model errors The <code>model_errors</code> directory contains summarised model errors for the training and testing errors for the models listed above. Reference data Reference electronic structure data computed for the BCC and FCC crystals, along the Bain path and for the relaxed vacancy is stored in the <code>reference_data</code> folder. The Hamiltonian and overlap matrices are stored as compressed binary HDF5 files. The format and metadata can be viewed with the <code>h5dump</code> utility, or read in using the supplied Julia code (or indeed from other languages). Predicted data The <code>predicted_data/FCC</code> and <code>predicted_data/BCC</code> folders contain HDF5 files with the results of all model predictions shown in the manuscript on the FCC and BCC crystal structures. <code>predicted_data/FCC-to-BCC</code> contains the results of predictions along the Bain path with the optimized model described in the manuscript and <code>predicted_data/vacancy</code> contains the vacancy calculations.

Supplement materials - An appraisal of Ulva (Ulvophyceae, Chlorophyta) taxonomy

Sequence alignments, supplement figures and tables for the manuscript "An appraisal of Ulva (Ulvophyceae, Chlorophyta) taxonomy"

Assessing land surface phenology in Araucaria-Nothofagus forests in Chile with Landsat 8/Sentinel-2 time series - Data and Material

This dataset contains the Enhanced Vegetation Index (EVI) data used in our research work about land surface phenology of Andean Araucaria-Nothofagus forests as well as the phenology information derived from it. Study area: Conguillío National Park, Chile Study period: 2016-2020 Description of datasets: conguillio.sen2.lnd8.evi.2016.2020.nc - A raster dataset (NetCDF) of EVI values (resolution 10m). EVI was calculated from Level-2 Sentinel-2 and Landsat 8 data. To ensure harmonization, the Landsat 8 data was resampled and reprojected to Sentinel-2 properties prior to the index calculation. evi_gb_beck_white.tif - A raster dataset (GeoTiff) of phenological metrics per year (resolution 10m). Metrics were derived by fitting a double logistic function (see Beck et al., 2006) to the smoothed and interpolated EVI pixel time series. Subsequently, the main phenological variables SOS (start of season) and EOS (end of season) were extracted using a 50% threshold value. The dataset itself is a result of the R package "greenbrown" and the layers are named accordingly (see https://greenbrown.r-forge.r-project.org/phenology.php). It is available as GeoTIFF and as R rasterfile. Details about the methodology and results describing this dataset can be found in the following publication: Kosczor, E., Forkel, M., Hernández, J., Kinalczyk, D., Pirotti, F. & Kutchartt, E., 2022. Assessing land surface phenology in Araucaria-Nothofagus forests in Chile with Landsat 8/Sentinel-2 time series. Int. J. Appl. Earth Obs. Geoinf. 112, 102862. https://doi.org/10.1016/j.jag.2022.102862

Evaluating CO2 breakthrough in a shaly caprock material: a multi-scale experimental approach

These are image datasets analysed in the publication "Evaluating CO2 breakthrough in a shaly a caprock material: a multi-scale experimental approach" by Stavropoulou and Laloui in 2022 in Scientific Reports, DOI: https://doi.org/10.1038/s41598-022-14793-8 <ol> <li>File 1 contains the 3D reconstructed x-ray tomography volumes analysed in the paper in bin 2. Note that image registration has to be performed before further analysis for accounting rigid body deformations (rotation and translation) that may have taken place between the scans during cell transport. All registrations have been performed using as reference scan 01.  </li> <li>FIle 2 contains the results of the digital volume correlation performed with the <a href="https://ttk.gricad-pages.univ-grenoble-alpes.fr/spam/intro.html">spam</a> tookit.</li> </ol>

gmap - qgis training material: Beagle Rupes (Mercury)

This dataset part of the Geology and Planetary Mapping Winter School 2022 featuring Beagle Rupes as a study area. Beagle Rupes is lobate scarp at Mercurys surface with a length of more than 600km cross-cutting an oval shaped crater.  We compiled a beginners – intermediate level training package for the area. The package includes several basemaps such as  Map Projected Basemap Reduced Data Record (BDR) (Hash 2013a), High-incidence East-illumination Basemap (HIE), Map-projected High-incidence West-illumination (HIW) (Hash 2015a),  Map Projected Low-Incidence Angle Basemap Reduced Data Record (LOI) (Hash 2013b), Map Projected Multispectral Reduced Data Record (MDR) Hash 2015b) and digital terrain model (DTM) (Becker et al., 2016). The data is cut to the area of interest and a training project is set up for QGIS.  The training package is designed as a group exercise with four adjacent tiles covering the Beagle Rupes area.

openmit-licenseJun 2022View details →

The segregation of recycled basaltic material within mantle plumes explains the detection of the X-Discontinuity beneath hotspots: 2D geodynamic simulations: Data

<pre>This repository accompanies the paper ``` The segregation of recycled basaltic material within mantle plumes explains the detection of the X-Discontinuity beneath hotspots: 2D geodynamic simulations by Martina Monaco, Juliane Dannberg, Rene Gassmoeller, Stephen Pugh ``` The global models presented in the manuscript were run using the following dependencies: ``` ----------------------------------------------------------------------------- -- This is ASPECT, the Advanced Solver for Problems in Earth's ConvecTion. -- . version 2.3.0-pre (master, 74e48be) -- . using deal.II 9.3.0 -- . with 32 bit indices and vectorization level 2 (256 bits) -- . using Trilinos 12.10.1 -- . using p4est 2.2.0 ----------------------------------------------------------------------------- ``` This repository contains: - The 'all_model_series' folder with the files used to analyze the depth averages. Each series (100, Aoki, Hefesto) has its own subfolder; - The 'plugins' folder, with the required plugin to run the models. To compile the plugin, navigate into this directory and follow the steps: 1. `cmake -D Aspect_DIR=PATH_TO_ASPECT` (replace `PATH_TO_ASPECT` with the directory where you compiled ASPECT). 2. `make` - The 'run_series' bash script, with the command to run multiple models at once. The user should modify: ° The input file name: INPUT_FILE=heterogeneity-several-blobs-INSERT-SERIES.prm ° The directory where ASPECT is located: srun --mpi=pmix_v2 $HOME/aspect/aspect-build/aspect --" echo -e $COMMAND | sbatch --job-name gs_${viscosity}_spacing_${blob_spacing} -p hpg2-compute -N 1 -n 32 -t 3-23:59:00 -o output_gs_${viscosity}_spacing_${blob_spacing}.%j -e error_gs_${viscosity}_spacing_${blob_spacing}.%j --constraint 'haswell|skylake' --mem-per-cpu '3gb' --distribution block; - The three parameter files (.prm), one per series; - Two .py files containing the scripts necessary to plot all the figures in the paper</pre>

Additional Material for Debiasing architectural decision-making: a workshop-based training approach

This additional material was created to enable full replication of the experiment described in the paper: "Klara Borowa, Maria Jarek, Gabriela Mystkowska, Weronika Paszko and Andrzej Zalewski: Debiasing architectural decision-making: a workshop-based training approach". The paper was accepted for publication at ECSA 2022 (The 16th European Conference on Software Architecture). The folder contains the following files: Coding-details.xlsx - the coding scheme, as well as coding results. Contains all code counts for each team participating in the experiment. Experiment-meeting-plan.pdf - contains the plan for the 3-hour meeting during which the experiment was conducted, as well as a timetable of when each meeting took place and who organised it. Debiasing-workshop-slides.pdf - slides used during the debiasing workshop. Debiasing-workshop-plan.pdf - detailed instruction for workshop organisers, what should be said and done during the workshop. Participants_questionnaire_and_results.xlsx - questionnaire questions used to obtain information about participants, as well as questionnaire results for all 44 participants.

gmap - qgis training material: Aram Chaos (Mars)

Aram Chaos is a more than 250 km large crater characterized by the presence of Chaotic Terrains forming mesas and knobs, associated with the outflow channel of Ares Vallis. The Chaotic Terrains are unconformably embayed and locally superposed by some layered hydrate minerals-bearing deposits.    The training package includes a complete HRSC coverage (including images and DEMs) (Neukum et al., 2004; Jaumann et al., 2007) and selected CTX (Malin et al., 2007), HiRISE (McEwen et al., 2007), and CRISM (Murchie et al., 2007) data. The area has been divided in 8 tiles each of one ‘stand.alone’ in terms of geology but at the same time ready for collaborative mapping purposes.

In-network data collection and data processing - Supplementary materials for deliverable D5.1 - EU-H2020 FET project 'Watchplant'

Supplementary material for D5.1 - Watchplant. Contains collected dataset from plant experiments with blue and red light stimuli, classification results based on statistical methods, and plots of the recorded electropotentials.

Intermediate data files from the compilation of Economy-wide Material Flow Accounts for the Domestic Extraction of abiotic materials

These files represent a selection of intermediary files from the compilation of material flow accounts on Domestic Extraction (DE) of abiotic materials. The main output of this compilation has been integrated in the UNEP IRP Global Material Flow Database (GMFD). These files include input files (e.g. IDs for data harmonization, or factors for data conversion), as well as output files (e.g. supplementary information, or detailed data accounts before aggregation and integration into the GMFD). Please note: These files are published exclusively for the purpose of making this information available to interested parties in a transparent and orderly fashion, in particular to other research projects who may have use for it. Therefore, these files are not associated with any publication and have not been adjusted or formatted with regard to any publications standards, i.e. they are uploaded exactly as they have been processed in the respective R Github repository of the underlying data compilation. The following description attempts to give a short overview of the respective types of files and their contents. For more detailed information on the data compilation, please refer to chapters 6, 8, and 10 in the <a href="https://resourcepanel.org/sites/default/files/irp_technical_annex_global_material_flows_database.pdf">technical report</a> of the GMFD.   Main data output (i.e. detailed material flow accounts) DE_met_min_fos_CCC_2021-11-04.csv: Data aggregated to the official categories (CCC/TCCC) used for integration into the GMFD. With IDs, without names (e.g. for materials and countries). DE_met_min_fos_CCC_with_names_2021-11-04.csv: Same as above, but with names. DE_met_min_fos_Detailed_2021-11-04.csv: Detailed accounts, as compiled, before final aggregation. With IDs, without names. DE_met_min_fos_Detailed_with_names_2021-11-04.csv: Same as above, but with names.   DE_met_min_fos_Detailed_2022-05-24.csv: Slightly revised version from May 2022. But not included in current GMFD version.   ID and concordance tables ccc_vs_mat_ids.csv: Concordance table for allocation of detailed material accounts to aggregated CCC accounts. country_ids.csv: General ID table for country IDs and names estimated_ids.csv: Material IDs which are assigned during application of ore estimation factors. geo_exist.csv: Table for consistent geographic adjustment of data for specific countries which have disintegrated over time (not including regions like Germany, Yemen, Ethiopia, Sudan, which all have been dealt with individually if necessary). material_ids.csv: General ID table for material IDs and names. source_country_ids.csv: Concordance table for country IDs (i.e. for allocation of harmonized IDs to source namings/IDs) source_material_ids.csv: Concordance table for material IDs (i.e. for allocation of harmonized IDs to source namings/IDs) source_unit_ids.csv: Concordance table for unit IDs (i.e. for allocation of harmonized IDs to source namings/IDs) unit_ids.csv: General ID table for unit IDs and names.   Conversion factors conversion_elements.csv: Factors applied for conversion from metal compounds to elemental metals. conversion_factors_units.csv: Factors applied for unit conversions.   Ore estimation factors <ul> <li>These factors represent content-to-ore ratios (i.e. "tonnes of extracted ore/mineral per ton of produced content”).</li> </ul> all_integrated_est_fac_2021-11-04.csv: Factors for estimation of metal and mineral ores (all which have been compiled/integrated) applied_est_fac_2021-11-04.csv: Factors for estimation of metal and mineral ores (which have actually been applied) average_metal_prices_1990-2020.csv: Metal prices applied in the compilation of estimation ratios. raw_metal_to_ore_ratios_fineprint.csv: Raw metal-to-ore ratios derived from FINEPRINT mining data. raw_metal_to_ore_ratios_snl.csv: Raw metal-to-ore ratios derived from SNL mining data.   Intermediate files from data processing all_interm_conv_integr_2021-12-13.csv: Harmonized, converted (units & elemental metals), integrated (i.e. without double counting). Before any estimations (ores & construction minerals) and before final cleaning/formatting. wmd_bgs_usgs_interm_harmonized_2021-12-13.csv: All harmonized raw data from WMD/BGS/USGS, before any further processing (i.e. with double counting).   Comparison data (for mining accounts from FINEPRINT project) data_for_comparison_2022-05-24.csv: Data set specifically compiled for comparison with accounts on production of mines from the FINEPRINT project. Has been applied for verification of data published in "Jasansky et al. (2022) An open database on global coal and metal mining". <ul> <li>Includes all available types of materials which have been reported (e.g. ores and metals and metal compounds) <ul> <li>meaning: also materials which are associated with each other, for example iron ore and iron (and would therefore have been selectively integrated/excluded in the GMFD compilation).</li> </ul> </li> <li>Excludes any double counting for the exact same material from different data sources</li> <li>Harmonized IDs, converted units</li> <li>Includes metal compounds approximated/converted from reported elemental metals (where possible)</li> </ul>   materialflows.net data_sunburst_material_profiles_20220607.csv: Full detail of data underlying the Sunburst visualization "Global Domestic Extraction in 2019, by material group" in section "Raw Material Profiles" on materialsflow.net. However, for all available years. Includes data on Domestic Extraction of biomass. Includes detail for CCC, MFA13+, MFA4+   Outliers overview_adjusted_outliers_2021-12-22.csv: Overview of outliers which were adjusted during final formatting.   Other approximation_tailings_detailed_2021-09-26.csv: Estimation of tailings based on reported amounts of ores/minerals and their respective contents.