cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17613/jezjx-e2t39
title: Supplement 1. Housing unit change by census designated place, 1970-2020

description: Supplement 1 for Cityscape Article: "Operationally defining 'shrinking cities' in the United States: Exploring differences in the cities identified as shrinking using different base years, date ranges, and geographic boundaries"
DOI: 10.15468/dl.6ve9a7
title: Occurrence Download

description: A dataset containing 1694 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is Colliuris pensylvanica (Linnaeus, 1758)",
 "Year is greater than or equal to 1950",
 {
 "or" : [
 "GadmGid is USA",
 "GadmGid is CAN",
 "GadmGid is MEX"
 ]
 }
 ]
}

 The dataset includes 1694 records from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008039-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.12702261
title: State of Nature layers for Water Availability and Water Pollution to support SBTN Step 1: Assess and Step 2: Interpret & Prioritize

description: There are multiple well-recognized and peer-reviewed global datasets that can be used to assess water availability and water pollution. Each of these datasets are based on different inputs, modeling approaches, assumptions, and limitations. Therefore, in SBTN Step 1: Assess and Step 2: Interpret & Prioritize, companies are required to consult different global datasets for a robust and comprehensive State of Nature (SoN) assessment for water availability and water pollution. 

To streamline this process, WWF, the World Resources Institute (WRI), and SBTN worked together to develop two ready-to-use unified layers of SoN – one for water availability and one for water pollution – in line with the Technical Guidance for Steps 1: Assess and Step 2: Interpret & Prioritize (July 2024). The main outputs contain the maximum values of Water Availability and of Water Pollution as well as the individual indicators' values. This information is available at different spatial resolutions, thus in two data formats: 1) a shapefile with values at HydroBasins (Pfafstetter level 6); and 2) an excel file with values at sub-national divisions (Adm1) and national divisions (Adm0). These datasets and complete documentation are publicly available for download below.
DOI: 10.5281/zenodo.12702055
title: State of Nature layers for Water Availability and Water Pollution to support SBTN Step 1: Assess and Step 2: Interpret & Prioritize

description: Version 1.1 (June 2024)

There are multiple well-recognized and peer-reviewed global datasets that can be used to assess water availability and water pollution. Each of these datasets are based on different inputs, modeling approaches, assumptions, and limitations. Therefore, in SBTN Step 1: Assess and Step 2: Interpret & Prioritize, companies are required to consult different global datasets for a robust and comprehensive State of Nature (SoN) assessment for water availability and water pollution. 

To streamline this process, WWF, the World Resources Institute (WRI), and SBTN worked together to develop two ready-to-use unified layers of SoN – one for water availability and one for water pollution – in line with the Technical Guidance for Steps 1: Assess and Step 2: Interpret & Prioritize (July 2024). The main outputs contain the maximum values of Water Availability and of Water Pollution as well as the individual indicators' values. This information is available at different spatial resolutions, thus in two data formats: 1) a shapefile with values at HydroBasins (Pfafstetter level 6); and 2) an excel file with values at sub-national divisions (Adm1) and national divisions (Adm0). These datasets and complete documentation are publicly available for download below.
DOI: 10.5281/zenodo.7797978
title: State of Nature layers for Water Availability and Water Pollution to support SBTN Step 1: Assess and Step 2: Interpret & Prioritize

description: There are multiple well-recognized and peer-reviewed global datasets that can be used to assess water availability and water pollution. Each of these datasets are based on different inputs, modeling approaches, assumptions, and limitations. Therefore, in SBTN Step 1: Assess and Step 2: Interpret & Prioritize, companies are required to consult different global datasets for a robust and comprehensive State of Nature (SoN) assessment for water availability and water pollution. 

To streamline this process, WWF, the World Resources Institute (WRI), and SBTN worked together to develop two ready-to-use unified layers of SoN – one for water availability and one for water pollution – in line with the Technical Guidance for Steps 1: Assess and Step 2: Interpret & Prioritize (July 2024). The main outputs contain the maximum values of Water Availability and of Water Pollution as well as the individual indicators' values. This information is available at different spatial resolutions, thus in two data formats: 1) a shapefile with values at HydroBasins (Pfafstetter level 6); and 2) an excel file with values at sub-national divisions (Adm1) and national divisions (Adm0). These datasets and complete documentation are publicly available for download below.
DOI: 10.5281/zenodo.15467190
title: Soil Health Data

description: Physical, chemical and biological attributes of soils from restored and unrstored streams as well as reference wetlands.
DOI: 10.5281/zenodo.15467191
title: Soil Health Data

description: Physical, chemical and biological attributes of soils from restored and unrstored streams as well as reference wetlands.
DOI: 10.5281/zenodo.15467520
title: Electronic Companion-Unlocking the Potential of Battery Charging and Swapping Stations Achieving Fully Flexible Operation via B2X and V2X Integration
DOI: 10.5281/zenodo.15467521
title: Electronic Companion-Unlocking the Potential of Battery Charging and Swapping Stations Achieving Fully Flexible Operation via B2X and V2X Integration
DOI: 10.15468/dl.qxw87r
title: Occurrence Download

description: A dataset containing 10003 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Nitraria L."
 ]
}

 The dataset includes 10003 records from 104 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008041-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jxnd9s
title: Occurrence Download

description: A dataset containing 751 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is Anisodactylus sanctaecrucis (Fabricius, 1798)",
 "Year is greater than or equal to 1950",
 {
 "or" : [
 "GadmGid is USA",
 "GadmGid is CAN",
 "GadmGid is MEX"
 ]
 }
 ]
}

 The dataset includes 751 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008043-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.28435394
title: Nuclear phylogenomics of <i>Aloe </i>and related genera (alooids) and Asphodelaceae

description: With &gt;700 species, <i>Aloe</i> and its generic kin (alooids) are a morphologically diverse group of leaf succulents with a wide range across Africa, Madagascar and the Arabian Peninsula. Species such as <i>Aloe vera</i> and <i>A. ferox</i> are cultivated at scale for natural products, whole foods, and cosmetics. Despite substantial α-taxonomy contributions, infrageneric classification of <i>Aloe</i> has remained unresolved. Molecular systematics has been compromised by the lack of informative characters in standard markers and high costs of obtaining informative nuclear loci from large genomes (e.g. &gt;15 Gbp); and the difficulty of obtaining quality DNA extractions from plant material of known provenance. Here these constraints are overcome with target capture sequencing that allows cost-effective sequencing of informative low-copy nuclear loci; and unlocks genetic resources from preserved specimens in herbaria as well as silica-dried plant tissues. Using a custom kit for alooids, 189 nuclear loci in 295 species, including 50 herbarium specimens, were sequenced to build a new phylogenomic framework for the big genus <i>Aloe</i> and 11 closely related genera in Asphodelaceae subfam. Alooideae. Available taxonomic classifications for <i>Aloe</i> sensu stricto were contrasted against the obtained topology in an effort to stabilise the taxonomy. Genus-level representations of the family Asphodelaceae were sequenced with the same tool. The new phylogenomic framework demonstrates the monophyly of the alooids and confirms recent classifications in which smaller genera (<i>Aloidendron</i>, <i>Aloiampelos</i>, <i>Aristaloe</i>, <i>Gonialoe</i>, and <i>Kumara</i>) are separated. The topology for <i>Aloe</i> is strongly correlated with geographic patterns, and to a much lesser extent with habit (growth form), and vegetative or reproductive morphology that are mainstays of α-taxonomy. Repeated incidents of adaptive radiation and niche specialisation appear to underlie species diversity in <i>Aloe</i>. This study illustrates the power of combined (nuclear) phylogenomic and taxonomic (morphological) inference, including the utility of herbarium genomics, in resolving the systematics of big genera.
DOI: 10.15468/dl.7nrtd6
title: Occurrence Download

description: A dataset containing 409 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Hesperotettix nevadensis Morse, 1903"
 ]
}

 The dataset includes 409 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008044-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.34hc97
title: Occurrence Download

description: A dataset containing 784 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Nitrophila S.Watson"
 ]
}

 The dataset includes 784 records from 63 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008045-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pbyw7b
title: Occurrence Download

description: A dataset containing 840797 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Euodice malabarica (Linnaeus, 1758), Curvularia australiensis (Bugnic. ex M.B.Ellis) Manamgoda, L.Cai & K.D.Hyde, Curvularia tsudae H.Deng, Y.P.Tan & R.G.Shivas, Cavia aperea Erxleben, 1777, Dingana alaedeus Henning G.A. & Henning S.F., 1984, Dingana bowkeri Trimen, 1806, Heteropsis perspicua (Trimen, 1873), Estigmene multivittata Rothschild, 1910, Dira clytus Linnaeus, 1764, Dira swanepoeli (van Son, 1939), Dira jansei Swierstra, 1909, Dingana dingana Trimen, 1873, Physcaeneura panda (Boisduval, 1847), Gegenes niso (Linnaeus, 1764), Prosapia tilaranensis Peck, Castro-Valderrama & Valdez, 2020, Sogatella albofimbriata (Muir, 1926), Delphacissa uncinata (Fieber, 1866), Cemus leviculus Fennah, 1964, Toya tuberculosa (Distant, 1916), Empoascanara ethiopica Dworakowska, 1972, Thriambus pondolandensis (Muir, 1926), Sogatella nigeriensis (Muir, 1920), Tagosodes cubanus (Crawford, 1914), Nisia atrovenosa (Lethierry, 1888), Gegenes pumilio (Hoffmannsegg, 1804), Aeropetes tulbaghia (Linnaeus, 1764), Platycercus elegans elegans, Cacatua tenuirostris (Kuhl, 1820), Mus musculus Linnaeus, 1758, Anas platyrhynchos f. domesticus, Sipha flava (Forbes, 1884), Anser anser f. domesticus, Neodusmetia sangwani (Subba Rao, 1957), Mycalesis milyas Hewitson, 1864, Baoris fatuellus (Hopffer, 1855), Streblote butiti (Bethune-Baker, 1906), Metarctia flaviciliata Hampson, 1907, Crorema fuscinotata Hampson, 1910, Cropera testacea Walker, 1855, Psalis pennatula Fabricius, 1793, Caligo martia Latreille & Godart, 1824, Gnophodes chelys Fabricius, 1793, Mahanarva spectabilis (Distant, 1909), Cedusa caribbensis Caldwell, 1950, Leucania loreyi (Duponchel, 1827), Aeneolamia contigua (Walker, 1851), Colpixys gigas Boucek, 1972, Oligosita balcluthae Viggiani & Laudonia, Orobanche centaurina Bertol., Megachile apicalis Spinola, 1808)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 840797 records from 1172 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008016-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21335/nmdc-nersc-2033620192
title: HiAOOS 2024: XCTD and XBT data collected in the Nansen and Amundsen Basins, August/September 2024

description: XTCD and XBT data collected during the 2024 HiAOOS deployment cruise with KV Svalbard, in the Nansen and Amundsen Basins. Part of the data was taken to extend the section along 30E carried out by the Norwegian Polar Institute from R/V Kronprins Haakon.
DOI: 10.14466/cefasdatahub.172
title: UK Marine Strategy Descriptor 11 ship noise assessment data for 2018 to 2022

description: Annual median broadband (63 Hz - 4 kHz) sound level data for UK waters for the years 2018-2022 inclusive. Data format is ASCII. These maps underpin the UK Marine Strategy 2024 assessment for Descriptor 11.2 (continuous anthropogenic sound). Images of these maps in PNG format are also provided for reference. A summary table is also included which describes percentages of assessment region areas exceeding sound levels above given decibels. Under the UK Marine Strategy, Good Environmental Status for Descriptor 11.2 is defined according to the “spatial distribution, temporal extent and levels of anthropogenic continuous low-frequency sound”. Since the spatial distribution of noise is required, noise mapping must be undertaken in order to assess the indicator. Although field monitoring of continuous sound in support of the Descriptor has been ongoing since 2018, such monitoring only provides data at point locations in the UK EEZ. To produce noise maps of continuous sound, it is necessary to couple this field monitoring at specific locations with spatial models of continuous sound levels. Such models are derived from data on sound sources (e.g. ship movements, wind speed) and the propagation of underwater sound in the environment (which depends on bathymetry, seabed composition, and water column properties).

The source levels of wind-generated noise were modelled based on Reeder et al. (2011) using wind speed data sourced from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim/ERA-5 global atmospheric reanalysis (ECMWF, 2021). Ship source levels were computed using the JOMOPANS-ECHO model (MacGillivray and de Jong, 2021) and satellite AIS data.

The sound propagation was modelled using the energy-flux method (Weston, 1971). Bathymetry data were extracted from EMODNET with 7.5” resolution, temperature and salinity data from the Copernicus Atlantic European North West Shelf Ocean Physics Analysis and Forecast product (0.016° x 0.016°, 33 depth levels, daily mean values) (Copernicus, 2019). Seabed acoustic properties were derived from the Hamilton model (Hamilton, 1987) using EMODNET seabed sediment data.
DOI: 10.15468/dl.ut9t9s
title: Occurrence Download

description: A dataset containing 37 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((84.35277 -6.25579,120.64723 -6.25579,120.64723 8.25579,84.35277 8.25579,84.35277 -6.25579))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Cyrtodactylus majulah Grismer, Wood & Lim, 2012"
 ]
}

 The dataset includes 37 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011643-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13130/rd_unimi/n9enq4/jzh45m
title: opt-75.txt

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/jze7kr
title: nodes_init-74.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.15468/dl.xud63s
title: Occurrence Download

description: A dataset containing 610547 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 {
 "or" : [
 "BasisOfRecord is Fossil",
 "BasisOfRecord is Living Specimen",
 "BasisOfRecord is Material sample",
 "BasisOfRecord is Human Observation"
 ]
 },
 {
 "or" : [
 "TaxonKey is one of (Plantae, Fungi, Viruses, , , , Cycadophyta, Lentisphaerae, Oomycota, Mycetozoa, Caldisphaera lagunensis, , Pyrodictium occultum, Hyperthermus butylicus, Pyrolobus fumarii, Acidilobus aceticus)",
 "PhylumKey is one of (, , Lentisphaerae, Oomycota, Mycetozoa, Caldisphaera lagunensis, , Pyrodictium occultum, Hyperthermus butylicus, Pyrolobus fumarii, Acidilobus aceticus)"
 ]
 }
 ]
}

 The dataset includes 610547 records from 233 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040220-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13130/rd_unimi/n9enq4/dhdyw5
title: nodes_init-72.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/horrgf
title: edges_final-98.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/4gln7x
title: edge_data_final-83.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/citgdw
title: force_disp_init-62.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/mllhso
title: edge_data_final-82.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/uoolq6
title: nodes_init-66.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/d9oirp
title: nodes_final-52.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/llihi7
title: nodes_final-82.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/qvig2v
title: edges_init-81.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/2by1wd
title: nodes_final-67.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/nqewje
title: edge_data_final-51.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/7psgtk
title: opt-97.txt

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/d2o1s9
title: force_disp_final-67.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/iubgca
title: edges_init-96.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/lspnl1
title: edge_data_final-64.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/5mzhee
title: edges_init-65.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/6ycrkj
title: nodes_final-79.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/pueqh0
title: edges_init-64.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/u9rpa6
title: opt-68.txt

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/zl4hjh
title: edges_final-71.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/oluzcj
title: edges_init-78.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/pqg4y8
title: nodes_final-98.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/civi31
title: force_disp_init-73.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/irghac
title: nodes_final-84.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/rmtnz9
title: force_disp_init-66.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/m3kajy
title: nodes_init-90.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/ctjcg6
title: edges_final-81.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/ypaw3m
title: edge_data_final-89.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/ty06zv
title: opt-55.txt

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/jjeccf
title: edges_init-51.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/u6sk2e
title: edge_data_final-52.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/c0ivuu
title: edges_init-50.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/sgrovq
title: force_disp_init-57.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/q5odtv
title: opt-74.txt

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/hhkdtz
title: edges_final-66.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/tg1yyf
title: force_disp_final-69.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/ijeuzt
title: edges_final-70.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/5zixvb
title: edge_data_final-93.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/qdfbke
title: force_disp_final-90.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/tlt9f2
title: force_disp_final-55.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/4defte
title: nodes_init-77.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/g2g6lq
title: opt-89.txt

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/nslumg
title: edges_init-92.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/jg3375
title: force_disp_init-84.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/riagtb
title: nodes_init-97.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/uzssx8
title: opt-90.txt

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/whlog7
title: force_disp_init-90.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/d8kmmu
title: edges_final-50.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/cb2lmg
title: nodes_final-88.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/wrou3a
title: force_disp_init-52.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/lb8l0x
title: nodes_final-94.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/sey52s
title: force_disp_init-80.dat

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/e5wdgv
title: opt-57.txt

description: 250729_SIMULATION_DATA/COMP-N_1.zip
DOI: 10.13130/rd_unimi/n9enq4/ldmf8v
title: nodes_init-106.dat

description: 250729_SIMULATION_DATA/TENS-M_1.zip