Find research datasets worth reusing

Input data and scenarios outputs of the Water Resources Research paper "Optimal economic spatial and temporal allocation of green and grey investment to address water security threats : Case Study - Velhas River Basin, Brazil"

Open the record for dataset details and reuse information.

Input and output data and code for PPS2 on 333 vertebrate and111 plant genome assemblies, and for DDS2+ on fungal genome assemblies

The datasets contain PPS2 and DDS2+ source and binary code, other scripts, and some input and output data. Please see the README file after unpacking it. The absolute paths in the scripts need to be modified in order to duplicate the results in this dataset. Most of the input and output data have to be removed from the datasets for quick uploading and downloading; otherwise, the datasets would exceed the 50-Gb size limit.

Example image and PRU_Reader output

Open the record for dataset details and reuse information.

opencc-by-nc-sa-4.0Nov 2023View details →

RRR input and output files corresponding to "Global patterns in river water storage dependent on residence time"

Corresponding peer-reviewed publication This dataset corresponds to all the RRR input and output files that were used in the study reported in: <ul> <li>Collins, E. L., C. H. David, R. Riggs, G. H. Allen, T. M. Pavelsky, P. Lin, M. Pan, D. Yamazaki, R. K. Meentemeyer, and G. M. Sanchez (2024), Global patterns in river water storage dependent on residence time.</li> </ul> When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein. Input datasets The dataset herein benefited from and was built upon other datasets: <ul> <li>MERIT-Basins (version 1.0) derived from MERIT-Hydro (version 0.7) available under a CC BY-NC-SA 4.0 license. <a href="https://www.reachhydro.org/home/params/merit-basins">https://www.reachhydro.org/home/params/merit-basins</a></li> <li>GLDAS VIC Land Surface Model L4 monthly 1.0 x 1.0 degree (V2.0) available under a CC0 license. <a href="https://science.nasa.gov/spd-41/">https://science.nasa.gov/spd-41/</a>. DOI: <a href="https://doi.org/10.5067/ZRIHVF29X43C">10.5067/ZRIHVF29X43C</a></li> <li>GLDAS Noah Land Surface Model L4 monthly 1.0 x 1.0 degree (V2.0) available under a CC0 license. <a href="https://science.nasa.gov/spd-41/">https://science.nasa.gov/spd-41/</a>. DOI: <a href="https://doi.org/10.5067/QN80TO7ZHFJZ">10.5067/QN80TO7ZHFJZ</a></li> <li>GLDAS Catchment Land Surface Model L4 monthly 1.0 x 1.0 degree (V2.0) available under a CC0 license. <a href="https://science.nasa.gov/spd-41/">https://science.nasa.gov/spd-41/</a>. DOI: <a href="https://doi.org/10.5067/SGSL3LNKGJWW">10.5067/SGSL3LNKGJWW</a></li> </ul> Known bugs and limitations in this dataset or the associated manuscript In the original files for MERIT-Basins (version 1.0) derived from MERIT-Hydro (version 0.7), as obtained from the pfaf_level_02 zip files, two river reaches do not have a corresponding catchment: COMID=31000001 and COMID=61000003. These two river reaches were removed from our analysis, hence bringing the total number of river reaches used from 2,938,143 to 2,938,141, the same value as the total number of catchments. In the original files for MERIT-Basins (version 1.0) derived from MERIT-Hydro (version 0.7), as obtained from the pfaf_level_02 zip files, the attribute table for the river reach with COMID=67072432 contains only four upstream reaches (COMID=64072433, COMID=64072735, COMID=64073772, and COMID=64073786), while the reach actually has five upstream reaches on the map (those above and COMID=64073793). This was modified herein because all connectivity in this dataset was created by associating upstream and downstream nodes rather than using the attribute table. <div> <div> <div> Our monthly dataset of river gauges with 95% daily data availability for 1980–2009 and with average discharge greater than or equal to 100 m3/s contains 1,148 stations. After mapping gauges to the MERIT Hydro river network using a joint criteria of 1) within a distance of 0.05 degrees and 2) uncorrected 30-yr average simulated discharge of the river reach within one order of magnitude relative to the observed average discharge of the gauge; the number of gauges reduces to 1129. In cases of multiple gauges per reach, we selected the gauge that had the closest observed average discharge to the uncorrected 30-yr averages simulated, resulting in 1,001 gauges. The dataset was further split in calibration gauges (702) and validation gauges (299). Three specific gauges are located in places with 0 accumulation of runoff in their specific sub-basin, they are located at river reaches with ID 41003992, 41001545, and 24005996. These three gauges were removed from the overall analysis at all gauges, resulting in 998 total gauges. All three gauges are part of the 299 validation gauges, not the 702 calibration gauges. They were retained in the analysis of 299 validation gauges.    </div> </div> </div> Files included in this version All monthly discharge observation files for 1148 gauges, in shapefile and CSV formats: <ul> <li>sites_1980-01_2009-12_100cms_095p.zip</li> <li>timeseries_obs_1980-01_2009-12_100cms_095p_monthly.zip</li> </ul> All CSV files corresponding to the hydrography of all pfaf_level_02 regions of MERIT-Hydro v0.7 Basins v1.0 (i.e. the 61 values of ii): <ul> <li>coords_pfaf_ii.zip</li> <li>kfac_pfaf_ii_1km_hour.zip</li> <li>k_pfaf_ii_low.zip</li> <li>k_pfaf_ii_nrm.zip</li> <li>k_pfaf_ii_hig.zip</li> <li>rapid_catchment_pfaf_ii.zip</li> <li>rapid_connect_pfaf_ii.zip</li> <li>rapid_coupling_pfaf_ii_GLDAS.zip</li> <li>riv_bas_id_pfaf_ii_topo.zip</li> <li>sort_pfaf_ii_topo.zip</li> <li>xfac_pfaf_ii_0.1.zip</li> <li>x_pfaf_ii_low.zip</li> <li>x_pfaf_ii_nrm.zip</li> <li>x_pfaf_ii_hig.zip</li> </ul> All netCDF4 files with monthly surface and subsurface runoff that were combined from GLDAS 2.0 and corresponding to the following land surface models for the years 1980 to 2009 (both included): <ul> <li>GLDAS_CLSM_M_1980-01_2009-12_utc.zip</li> <li>GLDAS_NOAH_M_1980-01_2009-12_utc.zip</li> <li>GLDAS_VIC_M_1980-01_2009-12_utc.zip</li> <li>GLDAS_ENS_M_1980-01_2009-12_utc.zip (the 3-model ensemble average based on the three above models).</li> </ul> All netCDF4 files with monthly lateral inflows (based on the GLDAS data above), corresponding to each one of the 61 values of ii, the pfaf_level_02 code: <ul> <li>m3_riv_pfaf_ii_GLDAS_CLSM_M_1980-01_2009-12_utc.zip</li> <li>m3_riv_pfaf_ii_GLDAS_NOAH_M_1980-01_2009-12_utc.zip</li> <li>m3_riv_pfaf_ii_GLDAS_VIC_M_1980-01_2009-12_utc.zip</li> <li>m3_riv_pfaf_ii_GLDAS_ENS_M_1980-01_2009-12_utc.zip</li> </ul> All netCDF4 files with monthly discharge (based on the m3_riv data above), corresponding to each one of the 61 values of ii, the pfaf_level_02 code:  <ul> <li>Qout_pfaf_ii_GLDAS_CLSM_M_1980-01_2009-12_utc.zip</li> <li>Qout_pfaf_ii_GLDAS_NOAH_M_1980-01_2009-12_utc.zip</li> <li>Qout_pfaf_ii_GLDAS_VIC_M_1980-01_2009-12_utc.zip</li> <li>Qout_pfaf_ii_GLDAS_ENS_M_1980-01_2009-12_utc.zip</li> </ul> All netCDF4 files with monthly storage (based on the Qout_pfaf_ii_GLDAS_ENS data above), corresponding to each one of the 61 values of ii, the pfaf_level_02 code, and to each characteristic value of residence time (low, nrm, hig): <ul> <li>V_pfaf_ii_GLDAS_ENS_M_1980-01_2009-12_utc_low.zip</li> <li>V_pfaf_ii_GLDAS_ENS_M_1980-01_2009-12_utc_nrm.zip</li> <li>V_pfaf_ii_GLDAS_ENS_M_1980-01_2009-12_utc_hig.zip</li> </ul> All shapefiles with MERIT Hydro (v0.7) Basins (v1.0) river network on which the 30-year mean of lumped discharge was appended (based on the Qout_pfaf_ii_GLDAS_ENS_M_1980-01_2009-12_utc.zip data above): <ul> <li>riv_pfaf_ii_MERIT_Hydro_v07_Basins_v01_GLDAS_ENS.zip</li> </ul> All monthly discharge observation files for 1148 gauges, in shapefile format, with observed monthly mean appended: <ul> <li>sites_1980-01_2009-12_100cms_095p_meanQ.zip</li> </ul> All monthly discharge observation files for 1001 gauges snapped on the MERIT Hydro (v0.7) Basins (v1.0) river network, in shapefile and CSV formats, corresponding to each one of the 61 values of ii, the pfaf_level_02 code:  <ul> <li>sites_1980-01_2009-12_100cms_095p_meanQ_COR_pfaf_ii.zip</li> <li>sites_1980-01_2009-12_100cms_095p_meanQ_COR.zip</li> <li>obs_tot_id_1980-01_2009-12_100cms_095p_meanQ_COR_pfaf_ii.zip</li> <li>Qobs_1980-01_2009-12_100cms_095p_meanQ_COR_pfaf_ii.zip</li> </ul> All monthly discharge observation files for 1001 gauges snapped on the MERIT Hydro (v0.7) Basins (v1.0) river network, separated into calibration (CAL) and validation (VAL) gauges, in shapefile and CSV formats, corresponding to each one of the 61 values of ii, the pfaf_level_02 code:  <ul> <li>sites_1980-01_2009-12_100cms_095p_meanQ_CAL.zip</li> <li>obs_bas_id_1980-01_2009-12_100cms_095p_meanQ_CAL_pfaf_ii.zip</li> <li>sites_1980-01_2009-12_100cms_095p_meanQ_VAL.zip</li> </ul> All netCDF4 files with monthly lateral inflows (based on the m3_riv_pfaf_ii_GLDAS_ENS_M_1980-01_2009-12_utc.zip data above) corrected using Long-Term Inverse Routing for calibration (CAL) gauges only and associated monthly discharge data, corresponding to each one of the 61 values of ii, the pfaf_level_02 code: <ul> <li>m3_riv_pfaf_ii_GLDAS_CAL_M_1980-01_2009-12_utc.zip</li> <li>Qout_pfaf_ii_GLDAS_CAL_M_1980-01_2009-12_utc.zip</li> </ul> All netCDF4 files with monthly lateral inflows (based on the m3_riv_pfaf_ii_GLDAS_ENS_M_1980-01_2009-12_utc.zip data above) corrected using Long-Term Inverse Routing for all correction (COR) gauges only and associated monthly discharge data, corresponding to each one of the 61 values of ii, the pfaf_level_02 code: <ul> <li>m3_riv_pfaf_ii_GLDAS_COR_M_1980-01_2009-12_utc.zip</li> <li>Qout_pfaf_ii_GLDAS_COR_M_1980-01_2009-12_utc.zip</li> </ul> All netCDF4 files with monthly storage (based on the Qout_pfaf_ii_GLDAS_COR_M_1980-01_2009-12_utc.zip data above), corresponding to each one of the 61 values of ii, the pfaf_level_02 code, and to each characteristic value of residence time (low, nrm, hig): <ul> <li>V_pfaf_ii_GLDAS_COR_M_1980-01_2009-12_utc_low.zip</li> <li>V_pfaf_ii_GLDAS_COR_M_1980-01_2009-12_utc_nrm.zip</li> <li>V_pfaf_ii_GLDAS_COR_M_1980-01_2009-12_utc_hig.zip</li> </ul> All shapefiles with MERIT Hydro (v0.7) Basins (v1.0) river network on which the 30-year mean of lumped discharge was appended (based on the Qout_pfaf_ii_GLDAS_COR_M_1980-01_2009-12_utc.zip data above): <ul> <li>riv_pfaf_ii_MERIT_Hydro_v07_Basins_v01_GLDAS_COR.zip</li> </ul> All shapefiles with MERIT Hydro (v0.7) Basins (v1.0) catchments that have been dissolved and of which the perimeter was extracted, corresponding to each one of the 61 values of ii, the pfaf_level_02 code; as well as global combined files: <ul> <li>cat_pfaf_ii_MERIT_Hydro_v07_Basins_v01_disso.zip</li> <li>cat_ MERIT_Hydro_v07_Basins_v01_disso.zip</li> <li>cat_pfaf_ii_MERIT_Hydro_v07_Basins_v01_perim.zip</li> <li>cat_ MERIT_Hydro_v07_Basins_v01_perim.zip</li> </ul> All shapefiles with MERIT Hydro (v0.7) Basins (v1.0) river network retaining only those reaches that flow to the global coast, corresponding to each one of the 61 values of ii, the pfaf_level_02 code: <ul> <li>riv_pfaf_ii_MERIT_Hydro_v07_Basins_v01_coast.zip</li> </ul>

Source code and model outputs for 'Increasing Aerosol Direct Effect Despite Declining Global Emissions'

Source code, model outputs and python scripts used for the publication of Hermant, A., Huusko, L., & Mauritsen, T: Increasing Aerosol Direct Effect Despite Declining Global Emissions

Phased VCF outputs for "HiPhase: Jointly phasing small, structural, and tandem repeat variants from HiFi sequencing"

The collection of raw and processed VCF files for the paper titled: "HiPhase: Jointly phasing small, structural, and tandem repeat variants from HiFi sequencing".Raw VCF files from DeepVariant, pbsv, and TRGT are included in the unphased subfolder. Outputs from WhatsHap and HiPhase are in their own subfolders. Three replicates of HG002 are included in all subfolders. Layout is as follows:<blockquote>hiphase_vcfs├── all_vcfs.md5├── hiphase│   ├── HG002-rep1.GRCh38.deepvariant.hiphase.vcf.gz│   ├── HG002-rep1.GRCh38.deepvariant.hiphase.vcf.gz.tbi│   ├── HG002-rep1.GRCh38.pbsv.hiphase.vcf.gz│   ├── HG002-rep1.GRCh38.pbsv.hiphase.vcf.gz.tbi│   ├── HG002-rep1.GRCh38.trgt.hiphase.vcf.gz│   ├── HG002-rep1.GRCh38.trgt.hiphase.vcf.gz.tbi│   ├── HG002-rep2.GRCh38.deepvariant.hiphase.vcf.gz│   ├── HG002-rep2.GRCh38.deepvariant.hiphase.vcf.gz.tbi│   ├── HG002-rep2.GRCh38.pbsv.hiphase.vcf.gz│   ├── HG002-rep2.GRCh38.pbsv.hiphase.vcf.gz.tbi│   ├── HG002-rep2.GRCh38.trgt.hiphase.vcf.gz│   ├── HG002-rep2.GRCh38.trgt.hiphase.vcf.gz.tbi│   ├── HG002-rep3.GRCh38.deepvariant.hiphase.vcf.gz│   ├── HG002-rep3.GRCh38.deepvariant.hiphase.vcf.gz.tbi│   ├── HG002-rep3.GRCh38.pbsv.hiphase.vcf.gz│   ├── HG002-rep3.GRCh38.pbsv.hiphase.vcf.gz.tbi│   ├── HG002-rep3.GRCh38.trgt.hiphase.vcf.gz│   └── HG002-rep3.GRCh38.trgt.hiphase.vcf.gz.tbi├── unphased│   ├── HG002-rep1.GRCh38.deepvariant.vcf.gz│   ├── HG002-rep1.GRCh38.deepvariant.vcf.gz.tbi│   ├── HG002-rep1.GRCh38.pbsv.vcf.gz│   ├── HG002-rep1.GRCh38.pbsv.vcf.gz.tbi│   ├── HG002-rep1.GRCh38.trgt.vcf.gz│   ├── HG002-rep1.GRCh38.trgt.vcf.gz.tbi│   ├── HG002-rep2.GRCh38.deepvariant.vcf.gz│   ├── HG002-rep2.GRCh38.deepvariant.vcf.gz.tbi│   ├── HG002-rep2.GRCh38.pbsv.vcf.gz│   ├── HG002-rep2.GRCh38.pbsv.vcf.gz.tbi│   ├── HG002-rep2.GRCh38.trgt.vcf.gz│   ├── HG002-rep2.GRCh38.trgt.vcf.gz.tbi│   ├── HG002-rep3.GRCh38.deepvariant.vcf.gz│   ├── HG002-rep3.GRCh38.deepvariant.vcf.gz.tbi│   ├── HG002-rep3.GRCh38.pbsv.vcf.gz│   ├── HG002-rep3.GRCh38.pbsv.vcf.gz.tbi│   ├── HG002-rep3.GRCh38.trgt.vcf.gz│   └── HG002-rep3.GRCh38.trgt.vcf.gz.tbi└── whatshap    ├── HG002-rep1.GRCh38.deepvariant.whatshap.vcf.gz    ├── HG002-rep1.GRCh38.deepvariant.whatshap.vcf.gz.tbi    ├── HG002-rep2.GRCh38.deepvariant.whatshap.vcf.gz    ├── HG002-rep2.GRCh38.deepvariant.whatshap.vcf.gz.tbi    ├── HG002-rep3.GRCh38.deepvariant.whatshap.vcf.gz    └── HG002-rep3.GRCh38.deepvariant.whatshap.vcf.gz.tbi3 directories, 43 files</blockquote>

Diablo model output for LES basal slope effects study

Open the record for dataset details and reuse information.

ModelE2.2 output used in "How Volcanic Aerosols Globally Inhibit Precipitation" study

Included are means among three 20-simulation ensembles from the NASA GISS ModelE2.2 Earth system model. This output was created in simulations generated by Kevin DallaSanta for the study <a href="https://doi.org/10.5194/acp-22-8843-2022">DallaSanta & Polvani (2022)</a>, with the data here being a subset used in <a href="https://doi.org/10.1029/2023GL107930">a 2024 study by McGraw & Polvani</a>: 1) a control (non-eruption) case 2) 20 Tg sulfur eruption simulations with sea surface temperatures (SSTs) fixed to those from individual control runs 3) 20 Tg sulfur eruption simulations with SSTs interactive (responsive to volcanic aerosols)

Simulation outputs for the study on the transition from freezing rain to ice pellets during WINTRE-MIX IOP4

Simulation outputs used in the study that investigates the transition from freezing rain to ice pellets during WINTRE-MIX IOP4. The name of each zip file represents the corresponding simulation described in the manuscript (i.e., CTL, MYJ, and MYJ_HM simulations). Due to the size limit, only variables used in the study are uploaded.

opencc-by-4.0Dec 2022View details →

Main MARINA-Nutrients model outputs for European river basins for the period of 2017-2020

The excel file "main_MARINA-Nutrients_model_outputs_for_Europe_2017-2020" contains main results of the publication (https://doi.org/10.15302/J-FASE-2023526). The model results are presented in unit of load (kg/yr) for the period of 2017-2020 per river basin. These results are obtained by model calculations following the equations that are provided in the "Materials and Methods" of the publication and in the supporting file. "Codebook.csv" file includes more details on the data.

opencc-by-4.0Jun 2023View details →

Model Output and Validation Data for Online Determination of GNSS Differential Code Biases using Rao-Blackwellized Particle Filtering

This dataset contains the outputs of 10 test runs of the A-CHAIM data assimilation model used to evaluate the performance of the bias estimation procedure used by the model. The 5-minute output files for each test run are included in a separate folder. The IGS DCBs in SINEX format and the various global ionospheric maps used in the study are included. This dataset also includes all of the processed GNSS data used in the study, which were used to generate the DCBs used in each test run. The in-situ electron density measurements from DMSP as well as the ionosonde data from GIRO which were used to measure the performance are also included.

opencc-by-4.0Mar 2024View details →

Uncoupling of Behavioral and Metabolic Twenty-Four-Hour Rhythms in Reindeer (Current Biology, Meier et al. 2024): Metabolomics analysis tables: Annotation list, pathway analysis outputs, target list for targeted peak exstraction and MS/MS spectra of annotated features

Metabolomics analysis tables: Annotation list, pathway analysis outputs, target list for targeted peak exstraction and MS/MS spectra of annotated features

opencc-by-4.0Feb 2024View details →

Predicting and enhancing the multiple output qualities in curved laser cutting of thin electrical steel sheets using an artificial intelligence approach

This dataset is experimental results from a research topic of Predicting and enhancing the multiple output qualities in curved laser cutting of thin electrical steel sheets using an artificial intelligence approach.

HiFiBGC run output on four datasets

This dataset contains select files/folders from output of HiFiBGC run on four HiFi metagenomic datasets. This dataset is used for the analyses available here https://github.com/ay-amityadav/HiFiBGC_analyses.

opencc-by-4.0Mar 2024View details →

NetCDF data matrix with Lagrangian dispersal simulation output

zipped NetCDF file (unzipped ~444GB) The NetCDF file contains a data matrix with the number of particles per bin (lon 0.015°, lat 0.01°) in the geographic area from -12°W - 10°E, and 47°N - 63°N. Particle numbers are labelled according to the three simulated scenarios ("period" 0-2, 14-28, 0-28 days after release), by station ("station" as named in stations.csv)", by year ("year" 2019-2022), and by day when the simulation was started ("offset", 000-122 days counting from 01.05.-31.08.) data variable: particle_number [111,007,858,176  values, float32] dimensions:  lon_bin [length 1468, float32] lat_bin [length 1601, float32] period [length 3, object '0-2','14-28','0-28'] station [length 32, object 'DK_044','FR_0206'....(all station names)] year [length 4, object '2019'...'2022'] offset [length 123, object '000'...'122']

opencc-by-4.0Apr 2024View details →

Supplementary material S5: Model outputs

Supplementary material S5 to Schmid, U., Frehner, M., Bugmann, H., 2024. Management recommendations for Alpine protection forests: the importance of regeneration quality and initial stand composition.

opencc-by-4.0Aug 2023View details →

SDO CLV Pipeline Output

This record contains the output generated by sdo-clv-pipeline for use in Palumbo et al. (submitted to ApJ) - "Characterizing Solar Center-to-Limb Radial-Velocity Variability with SDO"

opencc-by-4.0Apr 2024View details →

Multi-regional Input–output Table for the Global Emerging Economies (EMERGING)

EMERGING: Up-to-date and full-scale MRIO tables covering 135 sector code (the actual number of sectors is 134) in 245 economies. The EMERGING database is also designed to incorporate more official and publicly available data from national statistical institutes to ensure a high level of data quality, especially for these economies.  Please see the steps in the development of the database and reconciliation and validation of bilateral trade data and national statistics in: Huo, J., Chen, P., Hubacek, K., Zheng, H., Meng, J., & Guan, D. (2022). Full-scale, near real-time multi-regional input–output table for the global emerging economies (EMERGING). Journal of Industrial Ecology, 1– 15. <a href="https://doi.org/10.1111/jiec.13264">https://doi.org/10.1111/jiec.13264</a> EMERGING CO2 Inventory: CO2 emissions (Mt CO2eq) from fossil fuel combustion and energy consumption at the sectoral level in each region are available from the IEA database and CEADs emission inventory data. The EMERGING CO2 Inventory includes a total of 7 energy types: 1. Coal; 2. Natural gas; 3. Oil products; 4. Crude, NGL, Ref Feeds.; 5. Other; 6. Oil shale & oil sands; 7. Peat & Peat products. <div>End year: The current version of the time series for EMERGING MRIO data is from 2010, 2015 to 2019. We have completed a full update for the period of 2020 to 2022 and the relevant new MRIO table is currently in the testing phase. It will be publicly shared online soon.</div> <div> </div> Announcements These data are made freely available to the public and the scientific community in the belief that their wide dissemination will lead to greater understanding and new scientific insights. The availability of these data does not constitute publication of the data. The data providers rely on the ethics and integrity of the user to ensure that they receive fair credit for their work. If the data are obtained for potential use in a publication or presentation, we kindly ask you to cite them properly according to the instructions on the website. Queries about the EMERGING database and further collaboration can be addressed to: jing.j.meng@ucl.ac.uk or huojw20@mails.tsinghua.edu.cn

opencc-by-4.0Apr 2024View details →

Material parameter influence on solitary wave induced surface dilation: restart files, output files, videos and input scripts

Restart files, output files, videos and input scripts for Material parameter influence on the expression of Solitary-Wave-Induced Surface Dilation (Frizzell and Hartzell, 2024). Input files can be find at the linked repository (https://github.com/efrizz-umd/SID_sensitivity). Restart files and output files were made with LIGGGHTS (https://www.cfdem.com/liggghtsr-open-source-discrete-element-method-particle-simulation-code) and videos were made with OVITO (https://www.ovito.org/).