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Fig. 111. Distribution maps. A in Taxonomic revision of the genus Elmomorphus Sharp, 1888. II. Redescription of the genus and review of the species from India, Nepal, Bhutan, Myanmar, China, Thailand, Laos, Cambodia, and Vietnam (Coleoptera: Dryopidae)

Fig. 111. Distribution maps. A. Elmomorphus paramontanus Kodada, Selnekovič & Jäch sp. nov. B. E. cuneatus sp. nov. C. E. umphangicus Kodada, Selnekovič & Jäch sp. nov. D. E. parvulus sp. nov. E. E. sausai Kodada, Selnekovič & Jäch sp. nov. F. E. longitarsis sp. nov.

Fig. 109. Distribution maps. A in Taxonomic revision of the genus Elmomorphus Sharp, 1888. II. Redescription of the genus and review of the species from India, Nepal, Bhutan, Myanmar, China, Thailand, Laos, Cambodia, and Vietnam (Coleoptera: Dryopidae)

Fig. 109. Distribution maps. A. Elmomorphus fusiformis sp. nov. B. E. elmoides sp. nov. C. E. calvus sp. nov. D. E. hongkong sp. nov. E. E. depressus sp. nov. F. E. sulcatus Kodada, Selnekovič & Jäch sp. nov.

Fig. 108. Distribution maps. A in Taxonomic revision of the genus Elmomorphus Sharp, 1888. II. Redescription of the genus and review of the species from India, Nepal, Bhutan, Myanmar, China, Thailand, Laos, Cambodia, and Vietnam (Coleoptera: Dryopidae)

Fig. 108. Distribution maps. A. Elmomorphus striatellus Delève, 1968. B. E. siamensis Kodada, Selnekovič & Jäch sp. nov. C. E. parabrevicornis sp. nov. D. E. auripilosus sp. nov. E. E. auratus sp. nov. F. E. horaki Kodada, Selnekovič & Jäch sp. nov.

Fig. 110. Distribution maps. A in Taxonomic revision of the genus Elmomorphus Sharp, 1888. II. Redescription of the genus and review of the species from India, Nepal, Bhutan, Myanmar, China, Thailand, Laos, Cambodia, and Vietnam (Coleoptera: Dryopidae)

Fig. 110. Distribution maps. A. Elmomorphus corpulentus sp. nov. B. E. ellipticus sp. nov. C. E. mazzoldii sp. nov. D. E. bryanti Hinton, 1935. E. E. prosternalis Hinton, 1935. F. E. montanus (Grouvelle, 1913).

Fig. 112. Distribution maps. A in Taxonomic revision of the genus Elmomorphus Sharp, 1888. II. Redescription of the genus and review of the species from India, Nepal, Bhutan, Myanmar, China, Thailand, Laos, Cambodia, and Vietnam (Coleoptera: Dryopidae)

Fig. 112. Distribution maps. A. Elmomorphus bispinosus sp. nov. B. E. nepalensis Satô, 1981. C. E. punctulatus sp. nov. D. E. simplipes sp. nov. E. E. donatus Kodada, Selnekovič & Jäch sp. nov. F. E. paradonatus sp. nov.

Map sheet 25-132 Lipník n. B. - Results of soil chemical analyses and pH of soil leachate

Results of soil chemical analyses and pH of soil leachate from the 25-132 Lipník nad Bečvou map sheet

30 m annual soybean area map in China from 2000 to 2022

China is the world’s largest consumer and importer of soybeans, with rising domestic demand driving the expansion of production and cultivation areas since 2000. Knowing the spatial distribution of soybean, including the trends and interannual variability, is essential for yield estimation, agricultural planning, and ensuring national food security. However, a high-precision, long-term, national-scale spatial dataset for soybean cultivation areas of China remains unavailable. To address this gap, we developed the China Soybean Area (ChinaSoyA30m) dataset at a 30-m resolution—covering the years 2000–2022 at the national scale, using Landsat imagery and a combined phenology-based and machine learning approach. We analyzed time-series phenological characteristics of major crops across nine major agricultural regions in China and automatically generated annual training samples for supervised classifiers through a GWCCI-derived unsupervised method. To enhance the accuracy of these samples, we applied gap statistics, K-means clustering, and spectral angle mapping techniques to minimize noise and improve classification reliability. The supervised classification was conducted using a multi-random forest fusion strategy on the Google Earth Engine (GEE) platform, leveraging dense Landsat-5/7/8/9 data to generate yearly soybean maps. Our ChinaSoyA30m dataset showed strong correlation with official statistics at provincial, prefectural, and county levels, with R2 values of 0.95, 0.89, and 0.80, respectively.

Drone-based glacier mapping of Nordenskiöldbreen and Tunabreen in Svalbard, 2024

This database contains drone-based mapping data of two glaciers, Svalbard, Norway. The glaciers are Tunabreen in Templefjord and Nordenskiöldbreen in Billefjord. The dataset was generated using a structure-from-motion (SfM) method using drone-based imagery. The data was processed with Agisoft Metashape and the processed data consists of digital elevation models (DEMs) in georeferenced .TIF file format, orthomosaic maps in georeferenced .TIF file format, and textured 3D models in .STL and .JPG file format. In addition, a process report in .PDF file format is included for each dataset. Mapping was conducted with a DJI Mavic 3 Pro Enterprise. The mapping area covers the crevassed glacier fronts. Data collection was conducted on Nordenskiöldbreen in Spring 2024 (2024-04-29) and Tunabreen in Summer 2024 (2024-09-04).    00_ReadMe.txt  01_2024_Tunabreen_Images.zip - Contains all raw images.  01_2024_Nordenskiöldbreen_Images.zip- Contains all raw images.  02_2024_Tunabreen_DEM.tif - Contains the digital elevation model. 02_2024_Nordenskiöldbreen_DEM.tif- Contains the digital elevation model. 03_2024_Tunabreen_Ortho.tif - Contains the orthomosaic map. 03_2024_Nordenskiöldbreen_Ortho.tif - Contains the orthomosaic map. 04_2024_Tunabreen_Report.pdf - Contains the post-processing report.  04_2024_Nordenskiöldbreen_Report.pdf- Contains the post-processing report.  05_2024_Tunabreen_Model.stl - Contains the 3D model.  05_2024_Nordenskiöldbreen_Model.stl - Contains the 3D model.  06_2024_Tunabreen_Texture.jpg - Contains the 3D model texture.  06_2024_Nordenskiöldbreen_Texture.jpg - Contains the 3D model texture.

Early pyroxene crystallisation deep below mid-ocean ridges: Supplementary data of gabbro phase mapping

The data repository contains part of the original microscopy imagery datasets from optical microscopy, electron microscopy backscattered electron (SEM-BSE), and Synchrotron X-ray fluorescence microscopy (XFM) experiments presented in <a href="https://doi.org/10.1016/j.epsl.2025.119423">Ubide et al. (2025)</a> 'Early pyroxene crystallisation deep below mid-ocean ridges' by Teresa Ubide*, David T Murphy, Robert B Emo, Michael Jones, Marco Acevedo Zamora, and Balz S Kamber Specifically, it includes the QuPath software (<a href="https://www.nature.com/articles/s41598-017-17204-5">Bankhead et al., 2017</a>) project including rock (mid-ocean ridge olivine gabbro) thin sections 81-R5w and 80-R6w. The project contains the semantic image segmentation outputs generated with the <a href="https://qupath.readthedocs.io/en/stable/docs/tutorials/pixel_classification.html">Pixel Classifier</a> and MatLab script described in <a href="https://www.mdpi.com/2075-163X/13/2/156">Acevedo Zamora et al. 2023</a> (see <a href="https://github.com/marcoaaz/Acevedo-Kamber/tree/main/QuPath_generatingMaps">code repository</a>). Sample 80-R6w was segmented using image annotations in QuPath and an input comprising a false-colour Cr-Ti-Ca XFM image, cross-polarised light maximum intensity (XPL-max), and plane-polarised light (PPL-0 degrees) photomicrographs. Similarly, Sample 81-R5w used a false-colour Cr-Ti-Ca XFM image, <a href="https://github.com/marcoaaz/AcevedoEtAl._2024b_autoencoder">deep sparse autoencoder</a> image representation of XFM (after <a href="https://www.sciencedirect.com/science/article/pii/S0009254124000779?dgcid=rss_sd_all">Acevedo Zamora et al., 2024</a>), cross-polarised light (XPL-0 degrees), plane-polarised light (PPL-0 degrees) photomicrographs, and recoloured SEM-BSE (after <a href="https://www.mdpi.com/2075-163X/13/2/156">Acevedo Zamora et al. 2023</a>).  The segmentation of both samples provided a conservative estimate of the locations of relict clinopyroxene cores (~4% volume of cpx mask), mantles, and rims in a similar phase map colour scheme for better comparison. If there are questions regarding the utilisation of the data, contact Marco Acevedo (marco.acevedozamora@qut.edu.au ; maaz.geologia@gmail.com).

Offshore wind turbine damage probability maps and hub height TC wind speeds for U.S. Atlantic and Gulf Coasts exposed to historical and future tropical cyclones

Damage probability maps for offshore wind turbines exposed to tropical cyclones (TCs) under both historical and future climate scenarios along the U.S. Atlantic and Gulf Coasts are presented in this dataset. TCs are generated using <a href="../records/10392725" target="_blank" rel="noopener">The Risk Analysis Framework for Tropical Cyclones (RAFT)</a>, forced by <a href="https://pcmdi.llnl.gov/CMIP6/" target="_blank" rel="noopener">CMIP6</a> historical and future global climate simulations. Maximum wind speeds for 20- and 50-year TCs are processed through a <a href="https://www.sciencedirect.com/science/article/pii/S0960148120311423">fragility function</a> specific to offshore wind (OSW) turbines in order to estimate the probability of damage – specifically yielding and buckling – based on wind speed intensity.  Included data: <ul> <li>TC wind speeds: Peak 10-min mean hub height (90m) TC wind speed maps</li> <li>Damage states: Yielding and Buckling probability maps for OSW turbines</li> <li>Geographic coverage: U.S. Atlantic and Gulf Coasts (up to 200km from the shoreline)</li> <li>Time periods: Historic (1980-2014) and Future (2066-2100)</li> </ul> Methodology: <ul> <li>Tropical cyclone simulation: The RAFT TC model is used to simulate storms for historical and future climates using CMIP6 environmental conditions.</li> <li>TC impact metric: Wind speeds associated with 20- and 50-year return period TCs are used to estimate the aerodynamic and sea wave loading on OSW turbines.</li> <li>Fragility functions: Wind speeds are input into a fragility function developed for OSW turbines, estimating the probability of yielding and buckling damage.</li> <li>Damage probability maps: The results consist of eight (8) gridded damage probability maps representing the likelihoods of yielding and buckling to OSW turbines from 20- and 50-year TCs under historical and future climatic conditions.</li> </ul> Potential Uses: <ul> <li>Assessing the spatial vulnerability of OSW infrastructure to TCs</li> <li>Supporting decision-making for the design and siting of turbines</li> <li>Evaluating the impact of climate change on the risk of damage to OSW infrastructure</li> </ul> For further insights into this dataset, users are encouraged to refer to the associated paper: <a href="https://www.nature.com/articles/s43247-024-01887-6">https://www.nature.com/articles/s43247-024-01887-6</a> This dataset offers valuable insights into the potential impact of TCs on offshore wind infrastructure, aiding in risk assessment and resilience planning for the renewable energy sector.

Drone-based glacier mapping of Borebreen in Svalbard, 2024

This database contains drone-based glacier mapping data of Borebreen, Svalbard, Norway. The glacier is located in Isfjorden. The dataset was generated using a structure-from-motion (SfM) method using drone-based imagery. The data was processed with Agisoft Metashape and the processed data consists of digital elevation models (DEMs) in georeferenced .TIF file format, orthomosaic maps in georeferenced .TIF file format, and textured 3D models in .STL and .JPG file format. In addition, a process report in .PDF file format is included for each dataset. Mapping was conducted with a DJI Mavic 3 Pro Enterprise. The mapping area covers the crevassed glacier fronts. Data collection was conducted over three days in August/September 2024. The three collection dates were 2024-08-14, 2024-08-28, and 2024-09-25.

Systematic Mapping Extraction Data

Context: Agile methodologies use continuous delivery and adaptability to develop software that meets the needs of its users. However, such methods are prone to accumulate technical debt (TD).  Agile teams must balance the benefits and risks of incurring debt by managing TD items. Knowing how TD management is conducted in agile software teams can help agile practitioners increase their ability to handle TD items. Aims: To investigate, based on the state of the art, how agile software teams have managed existing TD items in their projects. Method: We carried out a systematic mapping study covering articles from 2010 to 2023. Results: Among the agile methodologies, Scrum was the most used for TD management. Regarding practices, agile teams employ mostly user stories and sprint backlogs to identify TD items. Sprints and sprint backlogs are used to monitor debt and refactoring to prevent and repay TD items. Conclusion: This study maps current knowledge on TD management in agile methodologies, serving as a starting point for new investigations in the area.

Frequency maps of Y-chromosomal haplogroups in Finland - out of all samples

Y-chromosomal regional enrichment maps for all haplogroups with at least 1% frequency in Finland out of all samples. A star (*) within the map and gray color in the forest plot indicates the frequency is inferred by combining samples from geographically closest regions due to low coverage of samples in the region.

opencc-zeroJan 2024View details →

Linked collectors and determiners for: Vermont Orthoptera Records from Historic Dot Maps.

Natural history specimen data linked to collectors and determiners held within, "Vermont Orthoptera Records from Historic Dot Maps". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/9d943d7e-f74c-464d-a723-7d20266191ce">https://bionomia.net/dataset/9d943d7e-f74c-464d-a723-7d20266191ce</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/9d943d7e-f74c-464d-a723-7d20266191ce">https://gbif.org/dataset/9d943d7e-f74c-464d-a723-7d20266191ce</a>. Formatted as a Frictionless Data package.

opencc-zeroJan 2024View details →

Linked collectors and determiners for: The genus Bittacus Latreille (Insecta: Mecoptera) in Brazil: key to species, distribution maps, new synonym, and three new species.

Natural history specimen data linked to collectors and determiners held within, "The genus Bittacus Latreille (Insecta: Mecoptera) in Brazil: key to species, distribution maps, new synonym, and three new species". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/0ea3c1bd-7938-4961-b91e-18b886ef1446">https://bionomia.net/dataset/0ea3c1bd-7938-4961-b91e-18b886ef1446</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/0ea3c1bd-7938-4961-b91e-18b886ef1446">https://gbif.org/dataset/0ea3c1bd-7938-4961-b91e-18b886ef1446</a>. Formatted as a Frictionless Data package.

Mapping the Green Product-Space in Mexico: From Capabilities to Green Opportunities

The aim of this paper is to examine the current and potential capability to promote the green economy in Mexico, simultaneously detecting new opportunities for diversification and “green” productive sophistication so that Mexican entities can move toward environmentally friendly ecological products. For this, we adopted a novel methodology to measure the productive capabilities of the green economy in Mexico, thereby discovering the green product space at a subnational scale. Economic complexity methods were used to estimate the Green Complexity Index (GCI) and the Green Complexity Potential (GCP) for 32 Mexican regions considering a time series from 2004 to 2018 and a set of data on international trade in ecological products. The main findings are reflected in a grid of the Green Adjacent Possible (GAP) and a heatmap that shows the “grasslands” (current green products by state). The results are likely to influence industrial policy and state innovation agendas. A limitation of this work is that it is based only on data from the formal, industrial, and regulated economy. The originality lies in the fact that there were no previous studies in the context analyzed, and the fecundity of the research reflects the need to expand the study with a focus on green business models.

Cloud feedback maps from CMIP5 and CMIP6 models

This repository contains maps of cloud feedbacks computed using up to three methodologies from two types of experiments conducted across CMIP5 and CMIP6 models. Methods: <ul> <li>The approximate partial radiative perturbation technique (shortwave cloud feedbacks only), denoted with 'APRP' in filename. </li> <li>The adjusted change in cloud radiative effect, denoted with 'adjCRE' in filename. </li> <li>The cloud radiative kernel method, in files without 'APRP' or 'adjCRE' in the name. </li> </ul> Experiments: <ul> <li>amip+4K experiments, known as amip4K and amip-p4K in CMIP5 and CMIP6, respectively</li> <li>abrupt CO2 quadrupling experiments, known as abrupt4xCO2 and abrupt-4xCO2 in CMIP5 and CMIP6, respectively</li> </ul> Data formats: <ul> <li>For cloud feedbacks derived using APRP and adjCRE methods, all models' maps are contained within single netcdf files.</li> <li>For cloud feedbacks derived using cloud radiative kernels, Individual netcdf files are provided for each model, which are compressed into a zip file.</li> </ul>

opencc-by-4.0Feb 2023View details →

Fig. 3. Character state maps for four traits. A in Phylogenetic revision of Dennstaedtioideae (Dennstaedtiaceae: Polypodiales) with description of Mucura, gen. nov.

Fig. 3. Character state maps for four traits. A, Spore shape: monolete (blue), trilete (yellow); B, Sorus position: abaxial (blue), marginal (yellow). C, Chromosome base number: 43 or 86 (light blue), 34 (blue), 46 or 47 (light green), 29 (green), 44 (pink), 30 (red), 31 (light orange), 32 (orange), 48 (light purple), 28 (purple), 56 (yellow), 26 (brown), 52 (magenta), 38 (aquamarine); D, Perpispore morphology: prominent ridges (light blue), prominent ridges and verrucae (blue), prominent ridges, verrucae + irregular reticles (light green), verrucae (orange), verrucae and ridges (green), regular reticles (pink), regular reticles + tubercles (red), echinae (purple), baculae (light yellow), ornamented verrucae (brown), rodlets (yellow), tubercles (aquamarine), rugulae (light brown). White circles indicate taxa for which character state data is missing.

opencc-by-4.0Feb 2023View details →

Fig. 2. Character state maps for four traits. A in Phylogenetic revision of Dennstaedtioideae (Dennstaedtiaceae: Polypodiales) with description of Mucura, gen. nov.

Fig. 2. Character state maps for four traits. A, Abaxial indusium present (blue) vs. absent (yellow); B, Adaxial indusium present (blue) vs. absent (yellow); C, Epipetiolar buds present (blue) vs. absent (yellow); D, Leaf buds present (blue) vs. absent (yellow). White circles indicate taxa for which character state data is missing.