cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7867002
title: Worldwide benchmark of modelled solar irradiance data annex

description: This data annex contains the supplementary data to the IEA PVPS Task 16 report "Worldwide benchmark of modeled solar irradiance data" from 2023. The dataset includes visualizations and tables of the results as well as information concerning the reference stations. The dataset contains the following type of files: StationList.xlsx: list of all stations, including their coordinates, climate zone, station code, continent, altitude AMSL, data source, number of available test data sets, station type (Tier-1 or Tier-2), and available calibration record. Result tables in folder “ResultTables”: Folders “climate_zones” and “continents” contain the tables described in Section 5.3. The filenames are “Component_metric_in_subgroup.html” with “component” DNI or GHI, “metric” describing the metric (see Table 3), and “subgroup” describing the continent or climate zone. World maps: The folder “Resultmaps” contains world maps of the metrics described in Section 5.2. Either four or three metrics, depending on the map, are included in each pdf. A legend describing the meaning of the point size is also included. Scatter plots of test vs. reference irradiance: The folder “Scatterplots” contains two folders, “DNI” and “GHI”, for the two investigated components. Three subfolders are also contained in these two folders: The subfolders “plotsPerSiteYear” contain plots named “scatOverviewCOMPONENT_SITEYYYY.png”, where “COMPONENT” is either DNI or GHI, SITE is the three-letter site abbreviation, and YYYY is the evaluated year. The png plots include the scatterplots for all test data sets evaluated for the case specified by the filename. The subfolders “plotsPerTestdataProvider” contain plots named “scatOverviewTESTDATASET_COMPONENTYYYY.png”, where “TESTDATASET” describes the test data set, “COMPONENT” is either DNI or GHI, and YYYY is the evaluated year. The png plots include the scatterplots for all sites evaluated for the case specified by the filename. The subfolders “plotsPerTestdataProviderSamePosPerStat” contain the same scatterplots as “plotsPerTestdataProvider”, but using a slightly different visualization method. Here, the position of each scatterplot for a given site within the plot is always the same. Although this yields many empty subplots and small scatterplots, it can be helpful to rapidly browse through the plots if only one or a few stations are of interest.
DOI: 10.5281/zenodo.7867003
title: Worldwide benchmark of modelled solar irradiance data annex

description: This data annex contains the supplementary data to the IEA PVPS Task 16 report "Worldwide benchmark of modeled solar irradiance data" from 2023. The dataset includes visualizations and tables of the results as well as information concerning the reference stations. The dataset contains the following type of files: StationList.xlsx: list of all stations, including their coordinates, climate zone, station code, continent, altitude AMSL, data source, number of available test data sets, station type (Tier-1 or Tier-2), and available calibration record. Result tables in folder “ResultTables”: Folders “climate_zones” and “continents” contain the tables described in Section 5.3. The filenames are “Component_metric_in_subgroup.html” with “component” DNI or GHI, “metric” describing the metric (see Table 3), and “subgroup” describing the continent or climate zone. World maps: The folder “Resultmaps” contains world maps of the metrics described in Section 5.2. Either four or three metrics, depending on the map, are included in each pdf. A legend describing the meaning of the point size is also included. Scatter plots of test vs. reference irradiance: The folder “Scatterplots” contains two folders, “DNI” and “GHI”, for the two investigated components. Three subfolders are also contained in these two folders: The subfolders “plotsPerSiteYear” contain plots named “scatOverviewCOMPONENT_SITEYYYY.png”, where “COMPONENT” is either DNI or GHI, SITE is the three-letter site abbreviation, and YYYY is the evaluated year. The png plots include the scatterplots for all test data sets evaluated for the case specified by the filename. The subfolders “plotsPerTestdataProvider” contain plots named “scatOverviewTESTDATASET_COMPONENTYYYY.png”, where “TESTDATASET” describes the test data set, “COMPONENT” is either DNI or GHI, and YYYY is the evaluated year. The png plots include the scatterplots for all sites evaluated for the case specified by the filename. The subfolders “plotsPerTestdataProviderSamePosPerStat” contain the same scatterplots as “plotsPerTestdataProvider”, but using a slightly different visualization method. Here, the position of each scatterplot for a given site within the plot is always the same. Although this yields many empty subplots and small scatterplots, it can be helpful to rapidly browse through the plots if only one or a few stations are of interest.
DOI: 10.5281/zenodo.7868082
title: SPVPANELEX: Dataset containing aerial orthoimages (covering 257.93 km2 of the Spanish territory, with a spatial resolution of 0.5 m) labelled with photovoltaic panel information for binary recognition and semantic segmentation

description: The data have been generated using scripts developed in Python with Open-Source libraries (GDAL/OGR and MapScript) to rasterize of vector cartography representing the photovoltaic (PV) panels instalations in urban, industrial, and rural areas. This PV panels cartography has been generated by manual digitalizing the PV panels found latest aerial orthofotographs available on June 1, 2021 from Plano Nacional de Ortofotografía Aérea (PNOA), produced by the National Geographic Institute of Spain, using the Web Map Service PNOA-MA.<br> <br> The dataset consists of 239,680 images of 256 × 256 pixels in size, in png format, labelled with Class_1: “Contains PV panel” and Class_2: “Does not contain PV panel”, that were pre-divided with a split criterion of 70:10:20%. in train, validation and test folders, respectively.<br> <br> The structure of the data is as follows:<br> 1-Panels-Ortho and 1-Panels-Mask contain the images featuring PV panels and their corresponding ground truth mask for training the semantic segmentation networks.<br> 1-Panels-Ortho and 2-NoPanels-Ortho contain images containing and not containing PV panels, for the training of binary recognition models of PV panels.<br> <br> Moreover, in each folder the structure is the same: train, test, validation containing 70%, 10% and 20% of the total images and masks of each type.<br> <br> 1-Panels-Ortho<br> |----Train<br> |----Test<br> -----Validation<br> <br> 1-Panels-Mask<br> |----Train<br> |----Test<br> -----Validation<br> <br> 2-NoPanels-Ortho<br> |----Train<br> |----Test<br> -----Validation
DOI: 10.5281/zenodo.7868081
title: SPVPANELEX: Dataset containing aerial orthoimages (covering 257.93 km2 of the Spanish territory, with a spatial resolution of 0.5 m) labelled with photovoltaic panel information for binary recognition and semantic segmentation

description: The data have been generated using scripts developed in Python with Open-Source libraries (GDAL/OGR and MapScript) to rasterize of vector cartography representing the photovoltaic (PV) panels instalations in urban, industrial, and rural areas. This PV panels cartography has been generated by manual digitalizing the PV panels found latest aerial orthofotographs available on June 1, 2021 from Plano Nacional de Ortofotografía Aérea (PNOA), produced by the National Geographic Institute of Spain, using the Web Map Service PNOA-MA.<br> <br> The dataset consists of 239,680 images of 256 × 256 pixels in size, in png format, labelled with Class_1: “Contains PV panel” and Class_2: “Does not contain PV panel”, that were pre-divided with a split criterion of 70:10:20%. in train, validation and test folders, respectively.<br> <br> The structure of the data is as follows:<br> 1-Panels-Ortho and 1-Panels-Mask contain the images featuring PV panels and their corresponding ground truth mask for training the semantic segmentation networks.<br> 1-Panels-Ortho and 2-NoPanels-Ortho contain images containing and not containing PV panels, for the training of binary recognition models of PV panels.<br> <br> Moreover, in each folder the structure is the same: train, test, validation containing 70%, 10% and 20% of the total images and masks of each type.<br> <br> 1-Panels-Ortho<br> |----Train<br> |----Test<br> -----Validation<br> <br> 1-Panels-Mask<br> |----Train<br> |----Test<br> -----Validation<br> <br> 2-NoPanels-Ortho<br> |----Train<br> |----Test<br> -----Validation
DOI: 10.13020/qewn-zh14
title: Supporting data for Dynamic Aliphatic Polyester Elastomers Crosslinked with Aliphatic Dianhydrides
DOI: 10.6073/pasta/daf37937fbcb19fcf4254c5b2d346b07
title: CBS05 Estimates of vegetation structure and composition collected on Konza Prairie watersheds and on the nearby Rannell’s Preserve

description: Data set includes estimates of vegetation structure and composition collected during ~monthly sampling events on Konza Prairie watersheds and on the nearby Rannell’s Preserve. Vegetation data were collected from three (prior to 2017) or 10 randomly-selected locations on each watershed; two from outside the 10-ha plot (see project abstract) and one inside the plot. We sampled vegetation on each watershed once a month, during May, June, and July. Additional vegetation data were collected from bird nest sites within ~3 days of nests failing. We used 5 sets of Daubenmire frame measures to determine percent cover of major plant functional groups (at the center of the plot and 5 m from center at the 4 cardinal directions). We estimated visual obstruction by placing a Robel Pole in the middle, and 5 m from the middle of the plot in each of the 4 cardinal directions. For each pole placement, we stood 4 m away with eye 1 m above the ground in each of 4 directions, and counting the highest 5-cm segment not completely obscured by vegetation. At nests, we also estimated the slope and aspect in the center of each plot.
DOI: 10.5281/zenodo.7868877
title: IODP Expedition 354 Moisture and Density

description: Moisture and density (MAD) data were acquired on ~10 mL sediment or rock samples by measuring three out of four material parameters: wet (saturated) mass, wet volume, dry mass, and/or dry volume after 24 h drying in a convection oven at 105 degrees C. From the moisture and volume measurements, the following phase relationships are calculated: wet and dry water content, wet bulk density, dry bulk density, grain density, porosity, and void ratio. The combination of measurements is defined by the submethod chosen: A, B, C, or D. Wet (A, B, or C) and dry (A, B, C, or D) mass is determined using motion-compensated balances. Wet volume is determined either by helium pycnometry (A) or by the sample's geometric dimensions using calipers (A or D). Dry volume (C or D) is measured by helium pycnometry. Submethods A and B are not recommended by IODP. Submethod C is suitable for saturated materials such as fine-grained sediments. Submethod D is suitable for unsaturated porous material such as certain limestones and basalts.
DOI: 10.5281/zenodo.7868876
title: IODP Expedition 354 Moisture and Density

description: Moisture and density (MAD) data were acquired on ~10 mL sediment or rock samples by measuring three out of four material parameters: wet (saturated) mass, wet volume, dry mass, and/or dry volume after 24 h drying in a convection oven at 105 degrees C. From the moisture and volume measurements, the following phase relationships are calculated: wet and dry water content, wet bulk density, dry bulk density, grain density, porosity, and void ratio. The combination of measurements is defined by the submethod chosen: A, B, C, or D. Wet (A, B, or C) and dry (A, B, C, or D) mass is determined using motion-compensated balances. Wet volume is determined either by helium pycnometry (A) or by the sample's geometric dimensions using calipers (A or D). Dry volume (C or D) is measured by helium pycnometry. Submethods A and B are not recommended by IODP. Submethod C is suitable for saturated materials such as fine-grained sediments. Submethod D is suitable for unsaturated porous material such as certain limestones and basalts.
DOI: 10.5281/zenodo.7868881
title: IODP Expedition 354 Magnetic susceptibility (whole round)

description: Magnetic susceptibility was measured on whole-round sections (and rarely section halves) on the Whole-Round Multisensor Logger (WRMSL) and/or Special Task Multisensor Logger (STMSL) using a Bartington MS2 meter and a 90 mm or 80 mm MS2C loop. As volume of the sample is not controlled for this experiment, susceptibility units are recorded in instrument units and are not volume-corrected.
DOI: 10.5281/zenodo.7868880
title: IODP Expedition 354 Magnetic susceptibility (whole round)

description: Magnetic susceptibility was measured on whole-round sections (and rarely section halves) on the Whole-Round Multisensor Logger (WRMSL) and/or Special Task Multisensor Logger (STMSL) using a Bartington MS2 meter and a 90 mm or 80 mm MS2C loop. As volume of the sample is not controlled for this experiment, susceptibility units are recorded in instrument units and are not volume-corrected.
DOI: 10.6084/m9.figshare.22704907
title: Autocorrelated Error with AR(1) Process

description: The autoregressive data set indicates data generated with autocorrelated error with AR(!) process
DOI: 10.6084/m9.figshare.22704907.v1
title: Autocorrelated Error with AR(1) Process

description: The autoregressive data set indicates data generated with autocorrelated error with AR(!) process
DOI: 10.5281/zenodo.7868887
title: IODP Expedition 354 Natural gamma radiation

description: Natural gamma radiation (NGR) data in the ~0.1 to 3.0 MeV range were measured using eight custom-designed sodium iodide (thallium) [NaI(Tl)] detectors arranged along the core measurement axis at 20 cm intervals. The NGR system uses layers of passive shielding (lead) and active shielding (plastic scintillators and coincidence electronics) to reduce the cosmic-ray signal for low-count analysis of sediment core sections and to obtain the maximum signal-to-noise ratio. Data are reported on a total counts per second basis and the raw spectral files are available as compressed files for later analysis.
DOI: 10.5281/zenodo.7868886
title: IODP Expedition 354 Natural gamma radiation

description: Natural gamma radiation (NGR) data in the ~0.1 to 3.0 MeV range were measured using eight custom-designed sodium iodide (thallium) [NaI(Tl)] detectors arranged along the core measurement axis at 20 cm intervals. The NGR system uses layers of passive shielding (lead) and active shielding (plastic scintillators and coincidence electronics) to reduce the cosmic-ray signal for low-count analysis of sediment core sections and to obtain the maximum signal-to-noise ratio. Data are reported on a total counts per second basis and the raw spectral files are available as compressed files for later analysis.
DOI: 10.5281/zenodo.7868892
title: IODP Expedition 354 Photomicrographs

description: Microscopic images of discrete samples were acquired using stereo and upright light microscopes and captured on digital cameras. Image files were uploaded along with a brief description and a record of the microscopic and lighting conditions when the image was taken.
DOI: 10.5281/zenodo.7868891
title: IODP Expedition 354 Photomicrographs

description: Microscopic images of discrete samples were acquired using stereo and upright light microscopes and captured on digital cameras. Image files were uploaded along with a brief description and a record of the microscopic and lighting conditions when the image was taken.
DOI: 10.5281/zenodo.7869262
title: IODP Expedition 354 Core orientation

description: Core orientation data were measured downhole during advanced piston corer (APC) coring, either by Minex FlexIT or Icefield MI-5 core orientation tool. These tools measure magnetic strength and orientation, 3-axis accelerometer data, and tool temperature data.
DOI: 10.5281/zenodo.7869261
title: IODP Expedition 354 Core orientation

description: Core orientation data were measured downhole during advanced piston corer (APC) coring, either by Minex FlexIT or Icefield MI-5 core orientation tool. These tools measure magnetic strength and orientation, 3-axis accelerometer data, and tool temperature data.
DOI: 10.5281/zenodo.7869267
title: IODP Expedition 354 Laser height profile (section half)

description: Height profile data were measured on the Section Half Multisensor Logger (SHMSL) by a rangefinding laser and recorded in uncorrected height units in millimeters in CSV files.
DOI: 10.5281/zenodo.7869266
title: IODP Expedition 354 Laser height profile (section half)

description: Height profile data were measured on the Section Half Multisensor Logger (SHMSL) by a rangefinding laser and recorded in uncorrected height units in millimeters in CSV files.
DOI: 10.5281/zenodo.7869269
title: IODP Expedition 354 P-wave velocity caliper (section/discrete)

description: P-wave velocity data were measured on undisturbed section halves (JRSO-defined x-axis) and/or discrete cube and cylinder samples (x, y, or z-axis) using pairs of piezoelectric transducers mounted on a caliper system. Report includes P-wave velocity in x, y, and/or z-direction, caliper separation, traveltime between transucers, and first arrival picks.
DOI: 10.5281/zenodo.7869268
title: IODP Expedition 354 P-wave velocity caliper (section/discrete)

description: P-wave velocity data were measured on undisturbed section halves (JRSO-defined x-axis) and/or discrete cube and cylinder samples (x, y, or z-axis) using pairs of piezoelectric transducers mounted on a caliper system. Report includes P-wave velocity in x, y, and/or z-direction, caliper separation, traveltime between transucers, and first arrival picks.
DOI: 10.5281/zenodo.7869273
title: IODP Expedition 354 P-wave velocity logger (whole round)

description: P-wave velocity data were measured on whole-round sections on the Whole-Round Multisensor Logger (WRMSL) using pairs of piezoelectric transducers mounted on a caliper system. Measurements may be affected by degassing of pore fluid and microfracturing during core recovery. Report includes P-wave velocity in x-y plane and distance and traveltime between transducers.
DOI: 10.5281/zenodo.7869272
title: IODP Expedition 354 P-wave velocity logger (whole round)

description: P-wave velocity data were measured on whole-round sections on the Whole-Round Multisensor Logger (WRMSL) using pairs of piezoelectric transducers mounted on a caliper system. Measurements may be affected by degassing of pore fluid and microfracturing during core recovery. Report includes P-wave velocity in x-y plane and distance and traveltime between transducers.
DOI: 10.6084/m9.figshare.22704922
title: Autocorrelated Error with Moving Average of order 1 process

description: The dataset contains data generated through the Moving Average process of order one in order to have data with autocorrelaed error.
DOI: 10.6084/m9.figshare.22704922.v1
title: Autocorrelated Error with Moving Average of order 1 process

description: The dataset contains data generated through the Moving Average process of order one in order to have data with autocorrelaed error.
DOI: 10.5281/zenodo.7850155
title: Comparison of microleakage under the orthodontic brackets bonded with five different adhesive systems: In vitro study

description: <strong>Abstract</strong> <em>Background:</em> Orthodontic treatment is associated with various risks, such as enamel discoloration, erosion, demineralization, and caries. The presence of microleakage between the enamel and adhesive and between the adhesive and the base of the orthodontic bracket allows penetration of the bacteria, molecules, and liquids into the enamel. The aim of this <em>in vitro</em> study was to evaluate microleakage in five adhesive systems commonly used in orthodontic practise for bonding brackets.
DOI: 10.5281/zenodo.7850154
title: Comparison of microleakage under the orthodontic brackets bonded with five different adhesive systems: In vitro study

description: <strong>Abstract</strong> <em>Background:</em> Orthodontic treatment is associated with various risks, such as enamel discoloration, erosion, demineralization, and caries. The presence of microleakage between the enamel and adhesive and between the adhesive and the base of the orthodontic bracket allows penetration of the bacteria, molecules, and liquids into the enamel. The aim of this <em>in vitro</em> study was to evaluate microleakage in five adhesive systems commonly used in orthodontic practise for bonding brackets.
DOI: 10.5281/zenodo.7869290
title: IODP Expedition 354 RGB channels (calculated from core photos)

description: Red, green, and blue pixel data were extracted from Section Half Imaging Logger (SHIL) linescan images, typically binned at 0.5 cm resolution using the central 2 cm of the image.
DOI: 10.5281/zenodo.7869289
title: IODP Expedition 354 RGB channels (calculated from core photos)

description: Red, green, and blue pixel data were extracted from Section Half Imaging Logger (SHIL) linescan images, typically binned at 0.5 cm resolution using the central 2 cm of the image.
DOI: 10.5281/zenodo.7869292
title: IODP Expedition 354 Color reflectance

description: Color reflectance data were measured on section halves using an integration sphere and a UV-VIS spectrophotometer mounted on the Section Half Multisensor Logger (SHMSL). Spectral counts are recorded in the range of 380 to 700 nm, covering the visible spectrum, and binned in ~2 nm bins. Spectral data are reduced from spectra and recorded in tristimulus XYZ values, CieLAB L*a*b* values, and other units.
DOI: 10.5281/zenodo.7869291
title: IODP Expedition 354 Color reflectance

description: Color reflectance data were measured on section halves using an integration sphere and a UV-VIS spectrophotometer mounted on the Section Half Multisensor Logger (SHMSL). Spectral counts are recorded in the range of 380 to 700 nm, covering the visible spectrum, and binned in ~2 nm bins. Spectral data are reduced from spectra and recorded in tristimulus XYZ values, CieLAB L*a*b* values, and other units.