cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7827625
title: Viviendas de lujo en venta de E&V en Barcelona

description: This <strong>dataset</strong> has <strong>2111</strong><strong> records</strong> and <strong>25 variables</strong> (the first one is an ID). All this information is from <strong>houses and flats for sale from</strong> the <strong>Barcelona province</strong> The information was extracted from <strong>www.engelvoelkers.com</strong> by using <strong>webscraping</strong> with <strong>selenium</strong>. We didn't perform a preprocessing, so some numerical variables are strings (<em>n_rooms, n_bedrooms, n_bathrooms, useful_area, built_area, land_area, price, energy_consumption, o2_emission, terrace_area</em>) <strong>The variables are:</strong> <em>id, eav_id, title, subtitle, n_rooms, n_bedrooms, n_bathrooms, useful_area, built_area, land_area, price, built_year, energy_class, energy_consumption, co2_emission, co2_emission_scale, protected, status, parking, garage, floor_cover, property_subclass, terrace_area, heating_type, location_status</em> <em><strong>This dataset is part of the practice 1 of the Tipología y ciclo de vida de los datos (Universitat Oberta de Catalunya)</strong></em>
description: {"references": ["Subirats Mat\u00e9, L. y Calvo Gonz\u00e1lez, M. (2019). Web Scraping [Material Digital].Tipolog\u00eda y ciclo de vida de los datos . Universidad Oberta de Catalunya."]}
DOI: 10.5281/zenodo.3725522
title: On-shelf nutrient trapping enhances the fertility of the southern Benguela upwelling system

description: The data submitted here is published in the manuscript entitled "On-shelf nutrient trapping enhances fertility of the southern Benguela upwelling system". Our data show that regenerated nutrients get “trapped” on the shelf of the southern Benguela upwelling system (SBUS), increasing the on-shelf nutrient pool available for upwelling. Nutrient trapping occurs when phytoplankton consume upwelled nutrients, sequestering them in their biomass, then sink and are decomposed on the shallow continental shelf, releasing nutrients to bottom waters. The nutrient-deplete surface waters subsequently flow offshore. SBUS nutrient trapping appears to be assisted by hydrographic fronts that limit the offshore transport of phytoplankton, such that their sinking and subsequent decomposition occurs on-shelf. Decomposition consumes oxygen, which means that enhanced nutrient trapping may increase oxygen depletion in the SBUS, with ecosystem-wide deleterious effects.
DOI: 10.5281/zenodo.3725521
title: On-shelf nutrient trapping enhances the fertility of the southern Benguela upwelling system

description: The data submitted here is published in the manuscript entitled "On-shelf nutrient trapping enhances fertility of the southern Benguela upwelling system". Our data show that regenerated nutrients get “trapped” on the shelf of the southern Benguela upwelling system (SBUS), increasing the on-shelf nutrient pool available for upwelling. Nutrient trapping occurs when phytoplankton consume upwelled nutrients, sequestering them in their biomass, then sink and are decomposed on the shallow continental shelf, releasing nutrients to bottom waters. The nutrient-deplete surface waters subsequently flow offshore. SBUS nutrient trapping appears to be assisted by hydrographic fronts that limit the offshore transport of phytoplankton, such that their sinking and subsequent decomposition occurs on-shelf. Decomposition consumes oxygen, which means that enhanced nutrient trapping may increase oxygen depletion in the SBUS, with ecosystem-wide deleterious effects.
DOI: 10.5281/zenodo.6415174
title: Reproduction package for the paper "The Apertif Radio Transient System (ARTS): Design, Commissioning, Data Release, and Detection of the first 5 Fast Radio Bursts"

description: This is a basic reproduction package for the paper "The Apertif Radio Transient System (ARTS): Design, Commissioning, Data Release, and Detection of the first 5 Fast Radio Bursts" by van Leeuwen et al. (2023). * arXiv:arXiv:2205.12362<br> * DOI: 10.1051/0004-6361/202244107
DOI: 10.5281/zenodo.6415175
title: Reproduction package for the paper "The Apertif Radio Transient System (ARTS): Design, Commissioning, Data Release, and Detection of the first 5 Fast Radio Bursts"

description: This is a basic reproduction package for the paper "The Apertif Radio Transient System (ARTS): Design, Commissioning, Data Release, and Detection of the first 5 Fast Radio Bursts" by van Leeuwen et al. (2023). * arXiv:arXiv:2205.12362<br> * DOI: 10.1051/0004-6361/202244107
DOI: 10.5281/zenodo.7827642
title: ArchaeoloGIS Open Dataset

description: “ARCHAEOLOGIS” A QGIS PROCESSING TOOLBOX<br> FOR ARCHAEOLOGISTS’ SPATIAL ANALYSIS License and Copyright:<br> Dataset for exercise and workshop distributed under CC BY-NC-SA 4.0 license. Cite the author and the name of the project. Paolo Rosati, ArchaeoloGIS dataset, 2023, and the DOI applied in Zenodo. Dataset description:<br> This dataset contains three geojson files:<br> - a road network with the ancient road system of the eastern part of Rome, mainly the Via Nomentana and the Via Tiburtina Valeria. It is a linear vector layer containing specific fields and basic metadata.<br> - a collection of places connected to the road network, mentioned in the ancient sources of the Tabula Peutingeriana and the Itinerarium Antonini. This point vector layer contains specific fields and metadata.<br> - a collection with proposed locations of milestones along the route of the road network. The milestones were automatically generated by the author of the dataset using the Tabula Peutingeriana script of the ArchaeoloGIS Plugin.<br> - The QGIS styles to manage the open dataset in QML format.<br> - The full Zotero source references for the plugin, and the open dataset folder.
DOI: 10.5281/zenodo.7827641
title: ArchaeoloGIS Open Dataset

description: “ARCHAEOLOGIS” A QGIS PROCESSING TOOLBOX<br> FOR ARCHAEOLOGISTS’ SPATIAL ANALYSIS License and Copyright:<br> Dataset for exercise and workshop distributed under CC BY-NC-SA 4.0 license. Cite the author and the name of the project. Paolo Rosati, ArchaeoloGIS dataset, 2023, and the DOI applied in Zenodo. Dataset description:<br> This dataset contains three geojson files:<br> - a road network with the ancient road system of the eastern part of Rome, mainly the Via Nomentana and the Via Tiburtina Valeria. It is a linear vector layer containing specific fields and basic metadata.<br> - a collection of places connected to the road network, mentioned in the ancient sources of the Tabula Peutingeriana and the Itinerarium Antonini. This point vector layer contains specific fields and metadata.<br> - a collection with proposed locations of milestones along the route of the road network. The milestones were automatically generated by the author of the dataset using the Tabula Peutingeriana script of the ArchaeoloGIS Plugin.<br> - The QGIS styles to manage the open dataset in QML format.<br> - The full Zotero source references for the plugin, and the open dataset folder.
DOI: 10.6084/m9.figshare.22494475.v2
title: Household appliances’ load profile generation based on data-analytic device operation and resident activities

description: This study discloses a load profile dataset of major household appliances for load flexibility (LF) utilization in residential sectors. To generate the profiles, measured load consumption patterns of each appliance and residents’ time-use survey data are analysed. Representative appliances used during the customers’ daily activities are defined and their power consumption profiles during a single operation cycle are collected according to various scenarios. The entire dataset is available at one-second intervals for daily profiles of individual appliances. The dataset can be used in multiple ways to contribute to the economic and environmental impact estimation of LF application, such as market/rate design, customer targeting, and electricity system planning.
DOI: 10.35003/mci3m3
title: Replication Data for: Numerical investigation of lunar basin formation constrained by gravity signature

description: • We provide relationships between impactor size, gravity signal and farside basin size as a function of cooling history and formation time; • Gravity anomalies serve as a measure of basin size as impacts altered morphology and crustal thickness models simplify subsurface structure; • Our basin formation models agree with the assumed formation time based on crater chronology and the estimated cooling history of the Moon.
DOI: 10.35003/mci3m3/a2oa4u
title: TableS1.tab

description: Determined diameter values from the numerical models: DLCT, Dt and DBA values for different impactor sizes depending on thermal state and crustal thickness.
DOI: 10.35003/mci3m3/hph8t5
title: TableS2.tab

description: Bouguer gravity anomalies: Central coordinates of lunar basins and its segments used for azimuthally averaged profiles with the corresponding Bouguer anomaly diameters based on different crustal densities.
DOI: 10.35003/mci3m3/4ucj19
title: TableS3.tab

description: Results of the DW-analysis with the corresponding modeled basin diameters for the investigated basins on the lunar farside.
DOI: 10.35003/mci3m3/dmp9qg
title: Fig5.tab

description: Data to reproduce Figure 4. Equations for the scaling laws are displayed in the Figure in the manuscript. Parameters: g_Moon=1.62 m/s2; v=13km/s; density of the target for Mimp calculation=2860 kg/m3.
DOI: 10.35003/mci3m3/wcszap
title: Fig6.tab

description: Data to reproduce Figure 6. Equations for derived trends are displayed in the Figure in the manuscript.
DOI: 10.35003/mci3m3/bfxtsm
title: Orientale_iSALE_modelmat.npz

description: Material fields (Fig. 7e)
DOI: 10.35003/mci3m3/gzpyds
title: Hertzsprung_iSALE_modelmat.npz

description: Material fields (Fig. 7b)
DOI: 10.35003/mci3m3/fdcmbx
title: 2021_07_03_Orientale_reproduced_Fig7.jpg

description: File produced by Fig_7.py
DOI: 10.35003/mci3m3/o8ifra
title: 2021_07_03_Hertzsprung_reproduced_Fig7.jpg

description: File produced by Fig_7.py
DOI: 10.35003/mci3m3/tmeosu
title: C60_melttemp.txt

description: Solidus Dc=60km
DOI: 10.35003/mci3m3/humqo4
title: 3800_C60_temperature.txt

description: Cold thermal profile (Dc=60km)
DOI: 10.35003/mci3m3/ro9t8d
title: 3800_C60_yield.txt

description: Cold target yield strength (Dc=60km)
DOI: 10.35003/mci3m3/pr54ft
title: 4100_temperature.txt

description: Intermediate thermal profile
DOI: 10.35003/mci3m3/8box0h
title: 4400_C60_yield.txt

description: Warm target yield strength (Dc=60km)
DOI: 10.35003/mci3m3/r2e6hp
title: 3800_C40_yield.txt

description: Cold target yield strength (Dc=40km)
DOI: 10.35003/mci3m3/3rmxtv
title: 4100_C40_yield.txt

description: Intermediate target yield strength (Dc=40km)
DOI: 10.35003/mci3m3/ttutbb
title: 4400_temperature.txt

description: Warm thermal profile
DOI: 10.35003/mci3m3/avhen9
title: Fig1.py

description: Python2 code to reproduce Fig.1.
DOI: 10.35003/mci3m3/rvywq8
title: 4100_C60_yield.txt

description: Intermediate target yield strength (Dc=60km)
DOI: 10.35003/mci3m3/kp2kgg
title: C40_melttemp.txt

description: Solidus Dc=40km
DOI: 10.35003/mci3m3/akmrw4
title: 2021_07_03_YieldTempConditions.jpg

description: File produced by running python2 code Fig1.py
DOI: 10.35003/mci3m3/e2pavd
title: 4400_C40_yield.txt

description: Warm target yield strength (Dc=40km)
DOI: 10.35003/mci3m3/ufebxy
title: 3800_C40_temperature.txt

description: Cold thermal profile (Dc=40km)
DOI: 10.35003/mci3m3/4cczky
title: 2021_07_03_Fig4.jpg

description: Output file after running code Fig4.py
DOI: 10.35003/mci3m3/4h2brc
title: 2021_07_03_Dtr-BADiam2.jpg

description: Fig. 8 reproduced by Fig8-10.py
DOI: 10.35003/mci3m3/2uk2fj
title: 2021_07_03_DLCT-BADiam2.jpg

description: Fig. 9a reproduced by Fig8-10.py
DOI: 10.35003/mci3m3/7isqie
title: 2021_07_03_BA-DLCT-Time2.jpg

description: Fig.10 reproduced by Fig8-10.py
DOI: 10.35003/mci3m3/5i9lor
title: 2021_07_03_BA-DLCT-L2.jpg

description: Fig. 9b reproduced by Fig8-10.py
DOI: 10.35003/mci3m3/lxgrba
title: Fig8-10.py

description: Python2 code to reproduce Figures 8-10. This code includes all separate basin information as well (Dt, DLCT, DBA,...) and the trendlines that are shown in the paper.
DOI: 10.35003/mci3m3/4xvbh5
title: Hertzsprung_iSALE_modelyc.tab

description: :unav
DOI: 10.35003/mci3m3/sqinkj
title: DWpath.txt

description: DW-Path (Fig.4b)
DOI: 10.35003/mci3m3/h5ynmg
title: DWmatrix.txt

description: DW-Matrix (Fig.4b)
DOI: 10.35003/mci3m3/vxma0j
title: Fig4_Gravity.txt

description: Gravity signatures (Fig. 4a)
DOI: 10.35003/mci3m3/jnhtfj
title: Fig7_Hertzsprung_DWmatrix.txt

description: DW-matrix (Fig. 7c)
DOI: 10.35003/mci3m3/6tkhqv
title: Fig7_Hertzsprung_Gravity.txt

description: Gravity (Fig. 7a)
DOI: 10.35003/mci3m3/6xn8ed
title: Fig7_Hertzsprung_DWpath.txt

description: DW-path (Fig. 7c)
DOI: 10.35003/mci3m3/nf2lyj
title: Fig7_Orientale_DWmatrix.txt

description: DW-matrix (Fig. 7f)
DOI: 10.35003/mci3m3/vcexbj
title: Fig7_Orientale_DWpath.txt

description: DW-path (Fig. 7f)
DOI: 10.35003/mci3m3/9nofh8
title: Hertzsprung_iSALE_modelcont.txt

description: Contourlines (Fig. 7b)
DOI: 10.35003/mci3m3/i1glax
title: Hertzsprung_iSALE_modelx.txt

description: x-Coordinates (Fig. 7b)
DOI: 10.35003/mci3m3/udailq
title: Hertzsprung_iSALE_modelxc.txt

description: Centered x-Coordinates (Fig. 7b)
DOI: 10.35003/mci3m3/ugzyyt
title: Hertzsprung_iSALE_modely.txt

description: y-Coordinates (Fig. 7b)
DOI: 10.35003/mci3m3/a9y3st
title: Hertzsprung_iSALE_modelyc.txt

description: Centered y-Coordinates (Fig. 7b)
DOI: 10.35003/mci3m3/bpvquv
title: Orientale_iSALE_modelcont.txt

description: Centered Coordinates x (Fig. 7e)
DOI: 10.35003/mci3m3/b0mes2
title: Orientale_iSALE_modelx.txt

description: x-Coordinates (Fig. 7e)
DOI: 10.35003/mci3m3/jqixzb
title: Orientale_iSALE_modelxc.txt

description: Centered x-Coordinates (Fig. 7e)
DOI: 10.35003/mci3m3/eol2jn
title: Orientale_iSALE_modely.txt

description: y-Coordinates (Fig. 7e)
DOI: 10.35003/mci3m3/hwuyqu
title: Fig7_Orientale_Gravity.txt

description: Gravity (Fig. 7d)
DOI: 10.35003/mci3m3/ssqr4x
title: Orientale_iSALE_modelyc.txt

description: Centered y-Coordinates (Fig. 7e)
DOI: 10.35003/mci3m3/f0nm75
title: Fig7.py

description: Python2 code to reproduce Fig.7a-c or Fig. 7d-f. The user has to enter the basin name at the beginning.
DOI: 10.35003/mci3m3/e4mi41
title: Fig4.py

description: Python2 code to reproduce Figure 4
DOI: 10.35003/mci3m3/57zvuf
title: Bulk_density_global.txt

description: Global bulk density
DOI: 10.35003/mci3m3/if6ijm
title: BA_global_varying_density.txt

description: Global Bouguer anomalies
DOI: 10.35003/mci3m3/rryhdt
title: Orientale_iSALE_modelmat.npz

description: Fig. 11b: Material distribution iSALE model
DOI: 10.35003/mci3m3/zh6sxc
title: Orientale_iSALE_modelyc.txt

description: Fig. 11b: Cell centered y-coordinates iSALE model
DOI: 10.35003/mci3m3/js18vg
title: Orientale_iSALE_modelxc.txt

description: Fig. 11b: Cell centered x-coordinates iSALE model
DOI: 10.35003/mci3m3/alu2er
title: Orientale_iSALE_modelx.txt

description: Fig. 11b: x-coordinates iSALE model
DOI: 10.35003/mci3m3/3quez9
title: 2021_07_05_Orientale_Fig11.jpg

description: Figure 11 created with Fig11.py
DOI: 10.35003/mci3m3/hdwykv
title: Fig11.py

description: Python2 code to reproduce Fig.11
DOI: 10.35003/mci3m3/rkct3e
title: Orientale_iSALE_modelcont.txt

description: Fig. 11b: Contour lines iSALE model
DOI: 10.35003/mci3m3/kuxm0s
title: Fig11_Orientale_DenGravity.txt

description: Fig. 11a right subplot: Gravity signal based on inhomogeneous density distribution
DOI: 10.35003/mci3m3/1fsopx
title: Orientale_iSALE_modelden.npz

description: Fig. 11b: Density distribution iSALE model
DOI: 10.35003/mci3m3/eiy9x6
title: Orientale_iSALE_modely.txt

description: Fig. 11b: y-coordinates iSALE model
DOI: 10.35003/mci3m3/ofiplc
title: Fig11_Orientale_Gravity.txt

description: Fig. 11a left subplot: Gravity signal based on constant density distribution & GRAIL measurements
DOI: 10.35003/mci3m3/xv2czb
title: Fig2.jpg

description: Figure 2: Definition of DLCT, DBA and D'BA
DOI: 10.35003/mci3m3/forrqo
title: Fig3.jpg

description: Figure 3: Gravity and density distribution of the lunar farside
DOI: 10.35003/mci3m3/qdwckb
title: 2021je006908-sup-0001-supporting information si-s01.pdf

description: :unav
DOI: 10.35003/y6mngd
title: Replication Data for: Nano- and micro-structures in lunar zircon from Apollo 15 and 16 impactites: implications for age interpretations

description: Abstract: Meteorite impact processes are ubiquitous on the surfaces of rocky and icy bodies in the Solar System, including the Moon. One of the most common accessory minerals, zircon, when shocked, produces specific micro-structures that may become indicative of the age and shock conditions of these impact processes. To better understand the shock mechanisms in zircon from Apollo 15 and 16 impact breccias, we applied transmission electron microscopy (TEM) and studied nano-structures in eight lunar zircons displaying four different morphologies from breccias 15455, 67915, and 67955. Our observations revealed a range of shock-related features in zircon: (1) planar and non-planar fractures, (2) “columnar” zircon rims around baddeleyite cores, (3) granular textured zircon, in most cases with sub-µm-size inclusions of monoclinic ZrO2 (baddeleyite) and cubic ZrO2 (zirconia), (4) silica-rich glass and metal inclusions of FeS and FeNi present at triple junctions in granular zircon and in baddeleyite, (5) inclusions of rutile in shocked baddeleyite, (6) amorphous domains, (7) recrystallized domains. In many grain aggregates, shock-related micro-structures overprint each other, indicating either different stages of a single impact process or multiple impact events. During shock, some zircons were transformed to diaplectic glass (6), and others (7) were completely decomposed into SiO2 and Zr-oxide, evident from the observed round shapes of cubic zirconia and silica-rich glass filling triple junctions of zircon granules. Despite the highly variable effect on textures and Zr phases, shock-related features show no correlation with relatively homogeneous U–Pb or 207Pb/206Pb ages of zircons. Either the shock events occurred very soon after the solidification or recrystallization of the different Zr phases, or the shock events were too brief to result in noticeable Pb loss during shock metamorphism.
DOI: 10.35003/y6mngd/xe6six
title: 410_2022_1977_MOESM1_ESM.pdf

description: :unav
DOI: 10.35003/y6mngd/zfvtdh
title: Figure 10 _ Nano- and micro-structures in lunar zircon from Apollo 15 and 16 impactites_ implications for age interpretations _ SpringerLink.pdf

description: :unav
DOI: 10.35003/y6mngd/r7tfpt
title: Figure 2 _ Nano- and micro-structures in lunar zircon from Apollo 15 and 16 impactites_ implications for age interpretations _ SpringerLink.pdf

description: :unav
DOI: 10.35003/y6mngd/cn7ktx
title: Figure 3 _ Nano- and micro-structures in lunar zircon from Apollo 15 and 16 impactites_ implications for age interpretations _ SpringerLink.pdf

description: :unav
DOI: 10.35003/y6mngd/zjodp0
title: Figure 7 _ Nano- and micro-structures in lunar zircon from Apollo 15 and 16 impactites_ implications for age interpretations _ SpringerLink.pdf

description: :unav
DOI: 10.35003/y6mngd/xsgmvu
title: Figure 6 _ Nano- and micro-structures in lunar zircon from Apollo 15 and 16 impactites_ implications for age interpretations _ SpringerLink.pdf

description: :unav
DOI: 10.35003/y6mngd/fz6rgv
title: Figure 8 _ Nano- and micro-structures in lunar zircon from Apollo 15 and 16 impactites_ implications for age interpretations _ SpringerLink.pdf

description: :unav
DOI: 10.35003/y6mngd/w5utcf
title: Figure 4 _ Nano- and micro-structures in lunar zircon from Apollo 15 and 16 impactites_ implications for age interpretations _ SpringerLink.pdf

description: :unav
DOI: 10.35003/y6mngd/ykiu6p
title: Figure 5 _ Nano- and micro-structures in lunar zircon from Apollo 15 and 16 impactites_ implications for age interpretations _ SpringerLink.pdf

description: :unav
DOI: 10.35003/y6mngd/2xjhuo
title: Figure 1 _ Nano- and micro-structures in lunar zircon from Apollo 15 and 16 impactites_ implications for age interpretations _ SpringerLink.pdf

description: :unav
DOI: 10.35003/y6mngd/cbq5ep
title: Table 1.tab

description: :unav
DOI: 10.35003/y6mngd/ratufh
title: Figure 9 _ Nano- and micro-structures in lunar zircon from Apollo 15 and 16 impactites_ implications for age interpretations _ SpringerLink.pdf

description: :unav
DOI: 10.5281/zenodo.7258998
title: CoUDlabs_WP8_T812_UDC_001. Identifying sediment deposits from temperature signals

description: This dataset contains the results of the experimental campaign and how data were collected on the research activity “Identifying sediment deposits from temperature signals” performed under Joint Research Activity “JRA 3- Improving Resilience and Sustainability in Urban Drainage solutions” of Co-UDlabs project, funded under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101008626. The experiments introduce a new approach which uses high-resolution temperature data to identify sediment bed deposits by analysing time series. Water (wastewater or drainage water in UDS) and sediments show different thermal properties and heat transfer dynamics. Thus, the aim of the dataset is to estimate or at least evidenced the presence of sediments by analyzing the differences between the temperature timeseries measured in the water phase and at the bottom of bed deposits. Results from this experimental campaign will help to understand heat transfer processes between water and saturated sediment mixtures and, therefore, develop new methodologies to estimate accumulation in UDS. The data are described so that others can use them.
DOI: 10.5281/zenodo.7258999
title: CoUDlabs_WP8_T811_UDC_001. Identifying sediment deposits from temperature signals

description: This dataset contains the results of the experimental campaign and how data were collected on the research activity “Identifying sediment deposits from temperature signals” performed under Joint Research Activity “JRA 3- Improving Resilience and Sustainability in Urban Drainage solutions” of Co-UDlabs project, funded under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101008626. The experiments introduce a new approach which uses high-resolution temperature data to identify sediment bed deposits by analysing time series. Water (wastewater or drainage water in UDS) and sediments show different thermal properties and heat transfer dynamics. Thus, the aim of the dataset is to estimate or at least evidenced the presence of sediments by analyzing the differences between the temperature timeseries measured in the water phase and at the bottom of bed deposits. Results from this experimental campaign will help to understand heat transfer processes between water and saturated sediment mixtures and, therefore, develop new methodologies to estimate accumulation in UDS. The data are described so that others can use them.