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90 results for “typology”
A global biophysical typology of mangroves version 3
<p>This dataset in an updated version of:</p> <p>Worthington, T. A. et al. A global biophysical typology of mangroves and its relevance for ecosystem structure and deforestation. Sci. Rep. 10, 14652 (2020)</p> <p>which delineates the world’s mangroves into geomorphic units based on their biophysical setting. Each unit consists of one or more patches of mangrove, grouped based on their proximity to macroscale coastal features, with these features determining their geomorphic class – deltaic, estuarine, lagoonal, and open coast.</p> <p> </p> <p>With the development of an updated mangrove extent timeseries (Global Mangrove Watch (GMW) v3.12), we updated the mangrove biophysical typology<sup>1</sup> to match this new extent. We firstly created an overlay between GMW v3.12 (all years combined to produce a 24-year composite extent) and the mangrove typology (v2.2) and identified those patches that were present in both. These patches were assigned to the same geomorphic class and individual geomorphic unit as the mangrove typology and provided the basis for the updated version. The patches present in the typology v2.2 but not in GMW v3.12 were deleted as they were no longer being mapped as mangrove in the GMW dataset.</p> <p> </p> <p>The patches now being mapped as mangrove in GMW v3.12 but had not been identified as such in the previous extent used to create the typology were then assigned to a geomorphic type and individual geomorphic unit using an iterative approach. Firstly, we identified patches that intersected with a single geomorphic unit and merged those patches to that unit, creating an enlarged unit extent. We repeated this procedure with the enlarged units, again enlarging their extent with patches only intersecting a single unit.</p> <p> </p> <p>We then used a series of distance buffers to identify unassigned patches that were within a certain distance of a single unit. After each step patches that were within the buffer distance of a single geomorphic unit were merged with that unit, and then the buffer was recalculated. The buffer distances were 1000m, 1000m, 1000m, 500m, 250m and 100m. Following the buffer, for the remaining unassigned patches we split them into those whose centroid was ≤ 10,000m from a geomorphic unit and those whose centroid was >10,000m for a geomorphic unit. As some of the patches were close (≤ 10,000m) from multiple geomorphic units, they were manually assessed and their assignment was corrected where necessary.</p> <p> </p> <p>The remaining patches (>10,000m from a geomorphic unit) were then visually assessed and can be split into three groups, 1) those part of large existing geomorphic units (only deltas, estuaries and lagoons) that were merged with that unit, 2) patches near deltas, estuaries and lagoons not mapped in the original GMW dataset, and 3) areas of open coast. The patches near deltas, estuaries and lagoons not mapped in the original GMW dataset resulted in the creation of 81 new geomorphic units. The open coast patches were aggregated into 268 clusters using a distance of 10,000m. Thirty-eight of the clusters were within 10,000m of an original open coast geomorphic unit and were merged with that unit. The remaining 230 were designated as new geomorphic units.</p> <p> </p> <p>We then undertook a process to merge open coast geomorphic units, by finding those small (<1km<sup>2</sup>) open coast geomorphic units that were within 10,000m of a larger one. Repeating the procedure to merge small open coast geomorphic units that were within 10,000m of another small open coast geomorphic unit. The final step was to do a visual assessment of all the units to remove errors. This was based around merging neighbouring geomorphic units of the same class if they represent the same system (e.g., one contiguous estuary or lagoon unit), assessed using high resolution imagery and the fluvial boundaries of the Hydrosheds basins<sup>2</sup>. Manually editing errors at unit boundaries where patches of one unit were surrounded by another unit. Splitting open coast units that overlapped another class of geomorphic unit e.g., an open coast unit with an estuary in the middle of it. Merging open coast units into large extents, particular those of the same section or aspect of the coast, using a distance of 10,000m as an approximate guide and trying not to create extents >100km<sup>2</sup>.</p> <p> </p> <p>A final publication version of the GMW dataset<sup>3</sup> (v3.14) was released <a href="https://zenodo.org/record/6894273">https://zenodo.org/record/6894273</a>, which differed slightly from v3.12. Firstly, a number of small areas of mangrove were removed at the edges of polygons, these were also removed from the typology. Secondly, additional areas of mangrove were mapped in the Persian Gulf, these new areas were merged with existing geomorphic units. These steps resulted in a final dataset ‘Mangrove Typology v3’ consisting of 3983 geomorphic units.</p> <p> </p> <p>1. Worthington, T. A. <em>et al.</em> A global biophysical typology of mangroves and its relevance for ecosystem structure and deforestation. <em>Sci. Rep.</em> <strong>10</strong>, 14652 (2020).</p> <p>2. Lehner, B. & Grill, G. Global river hydrography and network routing: Baseline data and new approaches to study the world’s large river systems. <em>Hydrol. Process.</em> <strong>27</strong>, 2171–2186 (2013).</p> <p>3. Bunting, P. <em>et al.</em> Global mangrove extent change 1996-2020: Global Mangrove Watch version 3.0. <em>Remote Sens.</em> <strong>14</strong>, 3657 (2022).</p> <p> </p>
Typology of agricultural land systems of Germany at a resolution of 100 hectares.
The decline of farmland biodiversity has widely been recognized in society and politics. Many factors that negatively affect biodiversity are associated with agriculture. European policy instruments and measures, which aimed at mitigating these impacts, have not been successful in counteracting the negative trends of farmland biodiversity. There is a growing recognition that conservation policy instruments need to be spatially targeted, given the heterogeneity of agricultural landscapes and extent of agricultural intensification in Europe. For Germany, we developed a typology of agricultural land systems (ALS) that captures the regional characteristics of agricultural intensification. For this purpose, we applied a cluster-analysis integrating indicators for land cover, landscape structure, land-use intensity, climate and relief at a resolution of with a spatial resolution of 1 km². As a result, we present a typology of eight ALS ranging from large-scale, intensive arable farming to extensive grassland/forest mosaics in mountains. The data included in this package contain the typology ALS and the corresponding values for the indicators for each hexagonal grid cell of 1 km²cell size. The typology of ALS could be used as a spatial framework for regional targeting of conservation policy instruments and for monitoring regional-specific trends of biodiversity indicators and their drivers. The data are supplement to the publication: Pingel, M; Sietz, D; Röder, N; Klimek, S and Golla, B. (2025) Typology of Agricultural Land Systems to Support Tailored Agri-Environmental Schemes for Farmland Biodiversity: A Case Study from Germany. [Preprint]. http://dx.doi.org/10.2139/ssrn.5162566.
Typology of antipassive coding patterns. Data
<p>MS-DOS CSV files with the data that have been used in the paper <strong>Typology of antipassive coding patterns </strong>by<strong> </strong>Ilja A. Seržant, Katarzyna Maria Janić, Darja Dermaku, Oneg Ben Dror.</p>
Typology of antipassive coding patterns and frequency effects of antipassives
Basic information about antipassives in languages with at least some ergative patterns
Nijmegen Typological Survey
<p>Cite the source of the dataset as:</p> <blockquote> <p>Harald Hammarström, Ger Reesink, Michael Dunn, Ruth Singer, Hedvig Skirgård, Suzanne van der Meer, Stephen C. Levinson. 2017. Nijmegen Typological Survey</p> </blockquote>
Fig. 12 in Emendations to tissue typology in discomycetes
Fig. 12 Short-celled excipular tissue types in different versions, viewed imbuta. g Textura globulosa incrassato-imbuta. h Textura angularis in radial section (a–c) or surface view (d–i). a Textura prismatica typica. b typica. i Textura angularis incrassata. Every square equals 50 × 50 μm. Textura prismatica incrassata. c Textura prismatica imbuta. d Textura Drawings by Esmée Winkel (a–c) and Erik-Jan Bosch (d–i) globulosa typica. e Textura globulosa incrassata. f Textura globulosa
Fig. 10 Pyrenopeziza rubi. a Habitus. b in Emendations to tissue typology in discomycetes
Fig. 10 Pyrenopeziza rubi. a Habitus. b Textura angularis typica in the ectal excipulum. Drawing by C. Bas, photograph by the author (coll. C. Bas no. 1442; 3.6.1958; Netherlands, Oegstgeest, "Oud-Poelgeest"; on dead, 3-year old stems of Rubus idaeus; herb. L 958.021–363)
Fig. 11 in Emendations to tissue typology in discomycetes
Fig. 11 Long-celled excipular tissue types in different versions, viewed g Textura intricata imbuta. h Textura epidermoidea typica. i Textura in radial section (a–g) or surface view (h–i). a Textura porrecta typica. b epidermoidea incrassata. Every square equals 50 × 50 μm. Drawings by Textura porrecta incrassata. c Textura porrecta imbuta. d Textura porrecta Esmée Winkel incrassato-imbuta. e Textura intricata typica. f Textura intricata incrassata.
Fig. 8 in Emendations to tissue typology in discomycetes
Fig. 8 Bulgaria inquinans. Textura intricata imbuta in medullary excipulum. Photograph by the author (coll. J. Hengstmengel no. 560; 27.10.2016; Netherlands, Breukelen, "Gunterstein"; on bark of dead logs of Quercus robur; herb. L.4314472)
Fig. 4 in Emendations to tissue typology in discomycetes
Fig. 4 Longitudinal section through the upper flank and margin of an apothecium of Cyathicula starbaeckii. The outer excipulum consists of textura oblita sensu Carpenter. Illustration by Susan Joyal (reproduced from Carpenter 1981, with permission of the publisher. © 1981, The New York Botanical Garden Press, Bronx, New York)
Fig. 7 Hymenoscyphus imberbis. a Habitus. b in Emendations to tissue typology in discomycetes
Fig. 7 Hymenoscyphus imberbis. a Habitus. b Textura intricata typica in medullary excipulum. Photographs by L.C.A.F. Rommelaars (coll. L.C.A.F. Rommelaars s.n.; 2013; Netherlands, Tilburg, "Kaaistoep"; on dead deciduous wood; herb. Rommelaars)
Fig. 9 Hymenoscyphus epiphyllus var. acarius. a Habitus. b in Emendations to tissue typology in discomycetes
Fig. 9 Hymenoscyphus epiphyllus var. acarius. a Habitus. b Textura globulosa typica in ectal excipulum. Photographs by L.C.A.F. Rommelaars (coll. L.C.A.F. Rommelaars s.n.; 2012; Netherlands, Tilburg, "Kaaistoep"; on mixture of Pinus sylvestris needles and deciduous leaves; herb. Rommelaars)
Fig. 3 in Emendations to tissue typology in discomycetes
Fig. 3 Textura oblita sensu Korf (reproduced from Korf 1958, with permission of the copyright holders). Remarkably the boundaries between two adjacent cells in the same hypha are not shown, which cannot be realistic
Fig. 2 in Emendations to tissue typology in discomycetes
Fig. 2 Surface view of the outer excipulum of Cyathicula starbaeckii showing textura oblita sensu Starbäck and at the margin calcium oxalate crystals (reproduced from Starbäck 1895)
Fig. 6 Cyathicula starbaeckii. a Habitus. b in Emendations to tissue typology in discomycetes
Fig. 6 Cyathicula starbaeckii. a Habitus. b Textura porrecta imbuta in ectal excipulum. Photographs by L.C.A.F. Rommelaars (coll. L.C.A.F. Rommelaars s.n.; 15.9.2012; Netherlands, Udenhout, "De Nieuwe Tiend"; on dying stemlets of Ranunculus; herb. Rommelaars)
Fig. 5 Hymenoscyphus fructigenus var. carpini. a Habitus. b in Emendations to tissue typology in discomycetes
Fig. 5 Hymenoscyphus fructigenus var. carpini. a Habitus. b Textura porrecta typica in the medullary excipulum. c Textura prismatica typica in the ectal excipulum. Drawing and photographs by the author (coll. J. Hengstmengel no. 443; 28.9.1979; Netherlands, Leiden, Botanical garden; on fallen fruits of Carpinus betulus; herb. L 977.215–219
Assessing the typology of person portmanteaus (supplementary material)
<p>Supplementary material for 'Assessing the typology of person portmanteaus' (doi:10.1007/s11525-017-9305-z)</p> <p> </p>
Dataset for the article "When possessor and argument indexes coincide. A typological survey" (Alessandra Barotto, Andrea Sansò)
<p><span>The dataset stored here contains the data underlying the article “When Possessor and Argument Indexes Coincide” (Alessandra Barotto, Andrea Sansò)<em>. </em>The dataset comes with a README file providing an explanation of the decisions made.</span></p>
Supplementary data for Plutniak, S. 2022. "What makes the identity of a scientific method? A history of the 'Structural and analytical typology' in the growth of evolutionary and digital archaeology in southwestern Europe (1950s–2000s)", Journal of Paleolithic Archaeology, vol. 5, 10.
<p>Supplementary data for Plutniak, S. 2022. “What makes the Identity of a Scientific Method? A History of the ‘Structural and analytical typology’ in the Growth of Evolutionary and Digital Archaeology in Southwestern Europe (1950s–2000s)”, <em>Journal of Paleolithic Archaeology</em>. vol 5, 10. DOI: <a href="https://doi.org/10.1007/s41982-022-00119-7">10.1007/s41982-022-00119-7</a>.</p> <p> </p> <p> </p> <p> </p>
Figure 3 in The effects of various lake typologies on the distribution of Chironomus spp. (Diptera), and arguments on optional factors of Water Framework Directive in Türkiye
Figure 3. CCA ordination of the sampled lakes (L1 to L20, except L2), significant environmental variables and species. Environmental variables ranging over (VIF> 10) were omitted from the ordination. A total of 56.62% variance was expressed in the first two axes. Groups (Grp I–IV) spotted by using K-means algorithm were mapped on the biplot. ORP: oxidation and reduction potential.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.