Find research datasets worth reusing

Weights for automatic two-dimensional bedrock fracture trace mapping from outcrop images

This file contains weights for automatic two-dimensional bedrock fracture trace mapping. The weights have been trained on manually mapped traces (https://doi.org/10.5281/zenodo.7077574) that were digitized from UAV-acquired drone orthomosaics from along the shoreline of Loviisa, South-East Finland (https://doi.org/10.5281/zenodo.7077518). Code for automatic mapping and weights generation is available on GitHub (https://github.com/nialov/ALSA). Data is published as Hierarchical Data Format, version 5 (HDF5). The work in automatic mapping was done as part of a Geological Survey of Finland project, Kallioperän Rikkonaisuus, during 2021-2022.

opencc-by-4.0Aug 2022View details →

PUMA IV: CO(2-1) channel maps

CO(2-1) channel maps of the 25 ULIRGs systems (38 individual nuclei) of the Physics of ULIRGs with MUSE and ALMA (PUMA) sample (Perna et al., 2021; Pereira-Santaella et al., 2021). These figures are an extended appendix to the paper Lamperti et al (2022), published in A&A (arXiv:2209.03380).

opencc-by-4.0Sep 2021View details →

Replication Data for "Mapping the Structure and Evolution of Software Testing Research Over the Past Three Decades"

In this research (publication included in the package), we have used author-assigned keywords as a quantitative data source for understanding the connections between keywords and research topics in software testing research, based on a large sample of studies from Scopus. We apply co-word analysis to map the topology of testing research as a network where author-assigned keywords are connected by edges indicating co-occurrence in publications. Keywords are clustered based on edge density and frequency of connection. We examine the most popular keywords, summarize clusters into high-level research topics, examine how topics connect, and examine how the field is changing. This package contains the map and network files used to perform our analyses, as well as the publication sample.

3DHD CityScenes: High-Definition Maps in High-Density Point Clouds

Overview 3DHD CityScenes is the most comprehensive, large-scale high-definition (HD) map dataset to date, annotated in the three spatial dimensions of globally referenced, high-density LiDAR point clouds collected in urban domains. Our HD map covers 127 km of road sections of the inner city of Hamburg, Germany including 467 km of individual lanes. In total, our map comprises 266,762 individual items. Our corresponding paper (published at ITSC 2022) is available <a href="https://www.researchgate.net/publication/364309881_3DHD_CityScenes_High-Definition_Maps_in_High-Density_Point_Clouds">here</a>. Further, we have applied 3DHD CityScenes to map deviation detection <a href="https://www.researchgate.net/publication/368983255_DNN-Based_Map_Deviation_Detection_in_LiDAR_Point_Clouds">here</a>.  Moreover, we release code to facilitate the application of our dataset and the reproducibility of our research. Specifically, our 3DHD_DevKit comprises: <ul> <li>Python tools to read, generate, and visualize the dataset,</li> <li>3DHDNet deep learning pipeline (training, inference, evaluation) for map deviation detection and 3D object detection.</li> </ul> The DevKit is available here: <a href="https://github.com/volkswagen/3DHD_devkit">https://github.com/volkswagen/3DHD_devkit</a>. The dataset and DevKit have been created by <a href="https://de.linkedin.com/in/christopher-plachetka-42b325115">Christopher Plachetka</a> as project lead during his PhD period at Volkswagen Group, Germany. When using our dataset, you are welcome to cite: <pre><code>@INPROCEEDINGS{9921866, author={Plachetka, Christopher and Sertolli, Benjamin and Fricke, Jenny and Klingner, Marvin and Fingscheidt, Tim}, booktitle={2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)}, title={3DHD CityScenes: High-Definition Maps in High-Density Point Clouds}, year={2022}, pages={627-634}}</code></pre> Acknowledgements We thank the following interns for their exceptional contributions to our work. <ul> <li><a href="https://www.linkedin.com/in/sertolli/">Benjamin Sertolli</a>: Major contributions to our DevKit during his master thesis</li> <li>Niels Maier: Measurement campaign for data collection and data preparation</li> </ul> The European large-scale project Hi-Drive (<a href="http://www.Hi-Drive.eu">www.Hi-Drive.eu</a>) supports the publication of 3DHD CityScenes and encourages the general publication of information and databases facilitating the development of automated driving technologies. The Dataset After downloading, the 3DHD_CityScenes folder provides five subdirectories, which are explained briefly in the following. 1. Dataset This directory contains the training, validation, and test set definition (train.json, val.json, test.json) used in our publications. Respective files contain samples that define a geolocation and the orientation of the ego vehicle in global coordinates on the map. During dataset generation (done by our DevKit), samples are used to take crops from the larger point cloud. Also, map elements in reach of a sample are collected. Both modalities can then be used, e.g., as input to a neural network such as our 3DHDNet. To read any JSON-encoded data provided by 3DHD CityScenes in Python, you can use the following code snipped as an example. <pre><code class="language-python">import json json_path = r"E:\3DHD_CityScenes\Dataset\train.json" with open(json_path) as jf: data = json.load(jf) print(data)</code></pre> 2. HD_Map Map items are stored as lists of items in JSON format. In particular, we provide: <ul> <li>traffic signs,</li> <li>traffic lights,</li> <li>pole-like objects,</li> <li>construction site locations,</li> <li>construction site obstacles (point-like such as cones, and line-like such as fences),</li> <li>line-shaped markings (solid, dashed, etc.),</li> <li>polygon-shaped markings (arrows, stop lines, symbols, etc.),</li> <li>lanes (ordinary and temporary),</li> <li>relations between elements (only for construction sites, e.g., sign to lane association).</li> </ul> 3. HD_Map_MetaData Our high-density point cloud used as basis for annotating the HD map is split in 648 tiles. This directory contains the geolocation for each tile as polygon on the map. You can view the respective tile definition using QGIS. Alternatively, we also provide respective polygons as lists of UTM coordinates in JSON. Files with the ending .dbf, .prj, .qpj, .shp, and .shx belong to the tile definition as “shape file” (commonly used in geodesy) that can be viewed using QGIS. The JSON file contains the same information provided in a different format used in our Python API. 4. HD_PointCloud_Tiles The high-density point cloud tiles are provided in global UTM32N coordinates and are encoded in a proprietary binary format. The first 4 bytes (integer) encode the number of points contained in that file. Subsequently, all point cloud values are provided as arrays. First all x-values, then all y-values, and so on. Specifically, the arrays are encoded as follows. <ul> <li>x-coordinates: 4 byte integer</li> <li>y-coordinates: 4 byte integer</li> <li>z-coordinates: 4 byte integer</li> <li>intensity of reflected beams: 2 byte unsigned integer</li> <li>ground classification flag: 1 byte unsigned integer</li> </ul> After reading, respective values have to be unnormalized. As an example, you can use the following code snipped to read the point cloud data. For visualization, you can use the pptk package, for instance. <pre><code class="language-python">import numpy as np import pptk file_path = r"E:\3DHD_CityScenes\HD_PointCloud_Tiles\HH_001.bin" pc_dict = {} key_list = ['x', 'y', 'z', 'intensity', 'is_ground'] type_list = ['<i4', '<i4', '<i4', '<u2', 'u1'] with open(file_path, "r") as fid: num_points = np.fromfile(fid, count=1, dtype='<u4')[0] # print(num_points) # Init for k, dtype in zip(key_list, type_list): pc_dict[k] = np.zeros([num_points], dtype=dtype) # Read all arrays for k, t in zip(key_list, type_list): pc_dict[k] = np.fromfile(fid, count=num_points, dtype=t) # Unnorm pc_dict['x'] = (pc_dict['x'] / 1000) + 500000 pc_dict['y'] = (pc_dict['y'] / 1000) + 5000000 pc_dict['z'] = (pc_dict['z'] / 1000) pc_dict['intensity'] = pc_dict['intensity'] / 2**16 pc_dict['is_ground'] = pc_dict['is_ground'].astype(np.bool_) fid.close() print(pc_dict) # Visualization # Normalize (due to large UTM values) x_utm = pc_dict['x'] - np.mean(pc_dict['x']) y_utm = pc_dict['y'] - np.mean(pc_dict['y']) z_utm = pc_dict['z'] xyz = np.column_stack((x_utm, y_utm, z_utm)) viewer = pptk.viewer(xyz) viewer.attributes(pc_dict['intensity']) viewer.set(point_size=0.03)</code></pre> 5. Trajectories We provide 15 real-world trajectories recorded during a measurement campaign covering the whole HD map. Trajectory samples are provided approx. with 30 Hz and are encoded in JSON. These trajectories were used to provide the samples in train.json, val.json. and test.json with realistic geolocations and orientations of the ego vehicle. <ul> <li>OP1 – OP5 cover the majority of the map with 5 trajectories.</li> <li>RH1 – RH10 cover the majority of the map with 10 trajectories.</li> </ul> Note that OP5 is split into three separate parts, a-c. RH9 is split into two parts, a-b. Moreover, OP4 mostly equals OP1 (thus, we speak of 14 trajectories in our paper). For completeness, however, we provide all recorded trajectories here.

opencc-by-4.0Jan 2022View details →

Compositional maps of the lunar polar regions derived from the Kaguya Spectral Profiler and the Lunar Orbiter Laser Altimeter data

Compositional maps of the lunar polar regions derived from the Kaguya Spectral Profiler and the Lunar Orbiter Laser Altimeter data as described in Lemelin et al. (2022).  This folder includes different GeoTIFF files described and shown in Lemelin et al. (2022). The files are projected in Polar Stereographic Projection, at a spatial resolution of 1000 m/pixel. A version of each of the following file is given for the north and south polar region (50-90 N/S). - Gridded and interpolated Spectral Profiler reflectance mosaics scaled to the LOLA dataset at 1064 nm - Data counts used in the gridded and interpolated Spectral Profiler reflectance mosaics scaled to the LOLA dataset at 1064 nm - Spectral Profiler FeO mosaics  - Spectral Profiler OMAT mosaics  - Spectral Profiler plagioclase mosaics  - Spectral Profiler olivine mosaics  - Spectral Profiler low-calcium pyroxene mosaics - Spectral Profiler high-calcium pyroxene mosaics  - Nanophase iron mosaics  - Correlation coefficient mosaics

FAIR Metrics SKOS Mapping

SKOS mapping between four FAIR metrics/indicators, based on "Best Effort". Created within the EOSC Synergy project task 3.3. <ul> <li>RDA FAIR data maturity model indicators</li> <li>FAIRsFAIR Data Object Assessment Metrics</li> <li>FAIR Maturity Indicators from FAIRsharing</li> <li>FAIR Enough data maturity indicators</li> </ul>

Supplementary datasets for quantitative fate mapping

Supplementary datasets for quantitative fate mapping: Dataset S1. All quantitative fate maps. Related to Figure 1.  Dataset S2. inDelphi predicted mutant allele probabilities for hgRNAs in MARC1 mice and iPSC line. Related to Figure 4 and Figure 7.   Dataset S3. Simulated phylogenies, single cell lineage barcodes, Phylotime reconstructed trees, fate map and set of MARC1 hgRNAs used for all experiments. Related to all Figures.

Spectroscopic ellipsometry mapping of PAAO:Au (AJ-3-04-20-Au sample)

Spectroscopic ellipsometry measurement data obtained from the porous anodized aluminum oxide covered with gold nanoparticles (PAAO:Au). The sample was made by anodization of aluminum monocrystal in 0.3 mol/L oxalic acid at 40 V for 4 minutes and 8 seconds. Then it was dip-coated in 60 nm diameter gold nanoparticles suspension. Extraction speed was 500 nm/s. The measurements were carried out at 20 × 20 locations covering all of the sample surface (approximately 4.8 × 4.8 mm2). The coordinates of each of 400 locations are available in "mapping_points.csv" file. All measurement data is also included in a single "AJ-3-04-20-Au Ellipsometry Mapping Measurements.rar" file. Ellipsometer: rotating compensator GES5-E (Semilab). Light source: 75 W xenon short arc lamp with 185-2000 nm wavelength spectrum. Detector: UV-Vis CCD with 0.8 nm resolution. Spectral range: approximately 230-960 nm. Light incidence angles: 50°, 55°, 60°, 65°, 70°, 75°. Light beam size: microspot (365 × 470 μm2 at 75° angle of incidence). The same sample was also measured using the same spectroscopic ellipsometry method before being covered with gold nanoparticles. The data can be found here: https://doi.org/10.5281/zenodo.5718424

Spectroscopic ellipsometry mapping of PAAO:Au (AJ-4-04-20-Au sample)

Spectroscopic ellipsometry measurement data obtained from the porous anodized aluminum oxide covered with gold nanoparticles (PAAO:Au). The sample was made by anodization of aluminum monocrystal in 0.3 mol/L oxalic acid at 40 V for 4 minutes and 30 seconds. Then it was dip-coated in 60 nm diameter gold nanoparticles suspension. Extraction speed was 500 nm/s. The measurements were carried out at 20 × 20 locations covering all of the sample surface (approximately 4.8 × 4.8 mm2). The coordinates of each of 400 locations are available in "mapping_points.csv" file. All measurement data is also included in a single "AJ-4-04-20-Au Ellipsometry Mapping Measurements.rar" file. Ellipsometer: rotating compensator GES5-E (Semilab). Light source: 75 W xenon short arc lamp with 185-2000 nm wavelength spectrum. Detector: UV-Vis CCD with 0.8 nm resolution. Spectral range: approximately 230-960 nm. Light incidence angles: 50°, 55°, 60°, 65°, 70°, 75°. Light beam size: microspot (365 × 470 μm2 at 75° angle of incidence). The same sample was also measured using the same spectroscopic ellipsometry method before being covered with gold nanoparticles. The data can be found here: https://doi.org/10.5281/zenodo.5734486

Connective Field maps via reverse-correlation

This data set contains processed CF and, where applicable, pRF mapping data for our project investigating the robustness of our CF reverse-correlation methods in the presence of eye movement and optical defocus. There are several participants in this archive. All of them participated in the LaserKiwi experiment in which they engaged in a video game, shooting coronaviruses with their eye gaze. Three participants (P4, P5, P6) also participated in the Unstable Eye experiments in which we used a classical sweeping bar pRF mapping design either with stable eye fixation in the screen centre or at a randomly jumping fixation dot. You will need SamSrf to read these files (version 9.3 recommended). It has been tested on Matlab R2020a & b.

Spectroscopic ellipsometry mapping of PAAO:Au (AJ-5-04-27-Au sample)

Spectroscopic ellipsometry measurement data obtained from the porous anodized aluminum oxide covered with gold nanoparticles (PAAO:Au). The sample was made by anodization of aluminum monocrystal in 0.3 mol/L oxalic acid at 40 V for 4 minutes and 57 seconds. Then it was dip-coated in 60 nm diameter gold nanoparticles suspension. Extraction speed was 500 nm/s. The measurements were carried out at 20 × 20 locations covering all of the sample surface (approximately 4.8 × 4.8 mm2). The coordinates of each of 400 locations are available in "mapping_points.csv" file. All measurement data is also included in a single "AJ-5-04-27-Au Ellipsometry Mapping Measurements.rar" file. Ellipsometer: rotating compensator GES5-E (Semilab). Light source: 75 W xenon short arc lamp with 185-2000 nm wavelength spectrum. Detector: UV-Vis CCD with 0.8 nm resolution. Spectral range: approximately 230-960 nm. Light incidence angles: 50°, 55°, 60°, 65°, 70°, 75°. Light beam size: microspot (365 × 470 μm2 at 75° angle of incidence). The same sample was also measured using the same spectroscopic ellipsometry method before being covered with gold nanoparticles. The data can be found here: https://doi.org/10.5281/zenodo.5734894

Story Map of the Technical Research Centre of Finland (VTT)

The Co-Change Lab at the Technical Research Centre of Finland (VTT) focuses on co-designing and implementing VTT’s sustainability programme. This work is a collaborative process that involves VTT’s sustainability responsibility researchers and the nominated responsibility task force.

opencc-by-4.0Oct 2022View details →

Story Map of the Council of Tampere Region on Responsible Regional Innovation Policy

The Co-Change Lab at the Council of Tampere Region focuses on developing regional funding to improve responsibility and sustainability.

Text-fig. 2. Map of Partizansk coal basin with early angiosperm localities. a: Severosuchan Formation, Aptian; b: Frentsevka Formation, early-middle Albian. 1 – Novoveselaya village; 2 – 3rd Kamenka River; 3 – Bolshoy Kuvshin; 4 – Andreev Inlet. in Angiosperm Diversification In The Early Cretaceous Of Primorye, Far East Of Russia

Text-fig. 2. Map of Partizansk coal basin with early angiosperm localities. a: Severosuchan Formation, Aptian; b: Frentsevka Formation, early-middle Albian. 1 – Novoveselaya village; 2 – 3rd Kamenka River; 3 – Bolshoy Kuvshin; 4 – Andreev Inlet.

opencc-by-4.0Feb 2022View details →

Text-fig. 1. Location of the study site. a: the location of Lühe Town, Yunnan, SW China; b: fossil bearing section, white arrow indicates the fossil collection stratum; c: geological map of fossil site. in Fraxinus L. (Oleaceae) Fruits From The Early Oligocene Of Southwest China And Their Biogeographic Implications

Text-fig. 1. Location of the study site. a: the location of Lühe Town, Yunnan, SW China; b: fossil bearing section, white arrow indicates the fossil collection stratum; c: geological map of fossil site.

Text-fig. 3. Map of Razdolnaya coal basin with early angiosperm localities. a: Lipovtsy Formation, Aptian; b: Galenki Formation, early-middle Albian. 1 – Porechye coal mine; 2 – Konstantinovka; 3 – Aleksee-Nikolskoe coal mine; 4 – Fedorovsky Rudnik; 5 – Podgorodenka coalfield; 6 – Firsov Cape. in Angiosperm Diversification In The Early Cretaceous Of Primorye, Far East Of Russia

Text-fig. 3. Map of Razdolnaya coal basin with early angiosperm localities. a: Lipovtsy Formation, Aptian; b: Galenki Formation, early-middle Albian. 1 – Porechye coal mine; 2 – Konstantinovka; 3 – Aleksee-Nikolskoe coal mine; 4 – Fedorovsky Rudnik; 5 – Podgorodenka coalfield; 6 – Firsov Cape.

Text-fig. 1. a: Idealised section of the Intra-Sudetic Basin (from Opluštil et al. 2016); b: Geological sketch map of the IntraSudetic Basin, here simplified (after Prouza and Tásler 2001, Pešek 2004). Explanations: 1 – Bohdašín Formation (Triassic), 2 – Bohuslavice Formation (Thuringian), 3 – Trutnov Formation (Saxonian), 4–13 Broumov Formation (Autunian): 4 – Martínkovice Member, 5 – Martínkovice Member with Jetřichovice, Hejtmánkovice and Vižňov horizons, 6–13 – Olivětín Member: 6 – Walchia bone coal facies, 7 – Basaltoides of the Šonov Group, 8 – Volcanoclastic facies, 9 – Aleuropelites, 10 – Ignimbrites, 11 – Rhyolite tuffs, 12 – Ruprechtice Limestone Horizon, 13 – Otovice Limestone Horizon. Localities: O1 – Otovice "Černý potok", O2 – Otovice "Stěnava", O3 – Otovice "Chmelnice", O4 – Otovice "Vápenka", R1 – Ruprechtice "Vápencové lomy", R2 – Ruprechtice "Pod Světlinou", R3 – Olivětín "Nad náhonem". in Actinopterygians Of The Broumov Formation (Permian) In The Czech Part Of The Intra-Sudetic Basin (The Czech Republic)

Text-fig. 1. a: Idealised section of the Intra-Sudetic Basin (from Opluštil et al. 2016); b: Geological sketch map of the IntraSudetic Basin, here simplified (after Prouza and Tásler 2001, Pešek 2004). Explanations: 1 – Bohdašín Formation (Triassic), 2 – Bohuslavice Formation (Thuringian), 3 – Trutnov Formation (Saxonian), 4–13 Broumov Formation (Autunian): 4 – Martínkovice Member, 5 – Martínkovice Member with Jetřichovice, Hejtmánkovice and Vižňov horizons, 6–13 – Olivětín Member: 6 – Walchia bone coal facies, 7 – Basaltoides of the Šonov Group, 8 – Volcanoclastic facies, 9 – Aleuropelites, 10 – Ignimbrites, 11 – Rhyolite tuffs, 12 – Ruprechtice Limestone Horizon, 13 – Otovice Limestone Horizon. Localities: O1 – Otovice "Černý potok", O2 – Otovice "Stěnava", O3 – Otovice "Chmelnice", O4 – Otovice "Vápenka", R1 – Ruprechtice "Vápencové lomy", R2 – Ruprechtice "Pod Světlinou", R3 – Olivětín "Nad náhonem".

Text-fig. 1. Map of the Isle of Wight with the distribution of the Wealden Group exposed on the south-western and south-eastern coast, showing the fossil localities mentioned in the text (in bold). in First Record Of Intact Equisetalean Strobili From The Wealden (Lower Cretaceous) Of The Isle Of Wight, Southern England

Text-fig. 1. Map of the Isle of Wight with the distribution of the Wealden Group exposed on the south-western and south-eastern coast, showing the fossil localities mentioned in the text (in bold).

Text-fig. 6. Shallowing pattern during the Middle Miocene to Late Miocene/Pliocene due to increasing magmatic activity as an external parameter. a: palaeobathymetry map during the Middle Miocene to Pliocene; b: sea level change curve indicating a shallowing pattern; c: relative changes of sea level and magmatic activity curve (Haq et al. 1987, Soeria-Atmadja et al. 1998, Muljana 2012). in Lithofacies And Ichnofacies Of Turbidite Deposits, West Java, Indonesia

Text-fig. 6. Shallowing pattern during the Middle Miocene to Late Miocene/Pliocene due to increasing magmatic activity as an external parameter. a: palaeobathymetry map during the Middle Miocene to Pliocene; b: sea level change curve indicating a shallowing pattern; c: relative changes of sea level and magmatic activity curve (Haq et al. 1987, Soeria-Atmadja et al. 1998, Muljana 2012).

Text-fig. 11. Permian ichthyofaunas from the French Massif Central. Preliminary comparisons based on total accounts of individuals (see text for explanations). Sharks in green, Acanthodes sp. in yellow, Actinopterygians in blue, Dipnoi in white (not visible but present at Autun; see Tab. 1 for details). Each circle is proportional to the total number of specimens recovered. Permian outcrops in black. Hercynian basement indicated by vertical lines. Map modified from Gand and Durand (2006). in New Actinopterygians From The Permian Of The Brive Basin, And The Ichthyofaunas Of The French Massif Central

Text-fig. 11. Permian ichthyofaunas from the French Massif Central. Preliminary comparisons based on total accounts of individuals (see text for explanations). Sharks in green, Acanthodes sp. in yellow, Actinopterygians in blue, Dipnoi in white (not visible but present at Autun; see Tab. 1 for details). Each circle is proportional to the total number of specimens recovered. Permian outcrops in black. Hercynian basement indicated by vertical lines. Map modified from Gand and Durand (2006).