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2,399 results for “Fragmentation”
British Library fragment Or.8210/S.9498 collated with དབའབཞེད་ (version 1.1)
<p>Comparison of BL S.9498+S.13683 with the DBA' BZHED MS (=DBA' 2000 in our referencing system). The purpose of the comparison is three fold: a) to tentatively reconstruct the disposition of lines and content across the folio from which the fragment came, b) to determine if there is enough space in the reconstructed folio to accommodate the names of the three ministers sent to investigate Śāntarakṣita, and c) to determine the likely position of ན་ visible as a tail in the top missing line.</p>
FragGT: Fragment-based Evolutionary Molecule Generation using Gene Types
<p>This directory contains data requires to run frag-gt (Meyers and Brown, 2023), a fragment-based evolutionary algorithm for generating optimal molecules released as part of the guacamol_baselines GitHub repository – https://github.com/BenevolentAI/guacamol_baselines.</p><p>Scripts for generating the data are available from github. The compressed data directory contains (A) Processed and filtered SMILES derived from ChEMBL v.33 produced by `download_chembl_smiles` and (B) fragment stores generated by `generate_fragstore` and `filter_fragstore` for both the above file in (A) and the original GuacaMol dataset.</p><p>smiles_files and fragstores were derived from molecule data downloaded from ChEMBL (https://www.ebi.ac.uk/chembl).</p><p>Liability: We do not represent and/or warrant that no third party rights exist which might prevent the use of the database or that no third party rights would be infringed by said use.</p><p>(data updated for frag-gt version 0.0.2)</p>
Biodiversity impact assessment considering land use intensities and fragmentation
<p>The data provide supplementary information for the paper entitled "Biodiversity impact assessment considering land use intensities and fragmentation".</p><p> </p><p><strong>Coverage of the characterization factors</strong></p><ul><li>5 species groups: plants, amphibians, birds, mammals, and reptiles</li><li>5 broad land use types: cropland, pasture, plantations, managed forests, and urban areas</li><li>3 land use intensities: minimal, light, intense (sometimes, intensity levels had to be merged because the data did not allow to differentiate between them)</li><li>825 terrestrial ecoregions of the world (according to WWF / Olson et al. 2001)</li></ul><p> </p><p><strong>Files</strong></p><p>Main data</p><ul><li>CF.csv: characterization factors (CFs) for ecoregions and 5 species groups</li></ul><p>Taxonomically aggregated data</p><ul><li>CF_kingdom.csv: CFs aggregated from 5 species groups to plant and animal kingdoms</li><li>CF_domain.csv: CFs aggregated from plant and animal kingdoms to the domain of Eukaryota</li></ul><p>Spatially aggregated data</p><ul><li>CF_country.csv: CFs aggregated from ecoregions to countries</li><li>CF_global.csv: CFs aggregated from ecoregions to the globe</li></ul><p>Taxonomically and spatially aggregated data</p><ul><li>CF_kingdom_country.csv: CFs aggregated to countries and plant and animal kingdoms</li><li>CF_domain_country.csv: CFs aggregated to countries and the domain of Eukaryota</li><li>CF_kingdom_global.csv: CFs aggregated to the globe and plant and animal kingdoms</li><li>CF_domain_global.csv: CFs aggregated to the globe and the domain of Eukaryota</li></ul><p> </p><p><strong>Units</strong></p><p>CFs for land occupation: PDF/m2</p><p>CFs for land transformation: PDF⋅yr/m2</p><p> </p><p><strong>Columns</strong></p><ul><li>realm: 2-letter code to identify one of 8 biogeographical realms</li><li>biome: ID to identify one of 14 biomes</li><li>eco_id: ID to identify the ecoregion, combining numbers for the realm, biome, and ecoregion within each biome nested within each realm</li><li>eco_name: ecoregion name</li><li>species_group: species group</li><li>kingdom: kingdom as a taxonomic rank</li><li>habitat_id: ID to link to the land use type and intensity as used in land_use.tif. An ID with .5 represents a merged land use class considering the two habitats with the IDs when rounding the value both up and down.</li><li>habitat: land use type and intensity</li><li>CF_*: characterization factor</li><li>*_occ*: land occupation</li><li>*_tra*: land transformation</li><li>*_avg*: average approach</li><li>*_mar*: marginal approach</li><li>*_reg: regional relative species loss</li><li>*_glo: global relative species loss</li><li>*_rsd: relative standard deviation as a measure of spatial uncertainty due to aggregation (only concerns country and globally aggregated CFs)</li><li>quality_*: data quality, distinguishing between original estimates and the use of proxies</li><li>objectid: object id of the country</li><li>iso3cd: iso3 code of the country</li><li>romnam: romanized name of the country</li><li>m49code: M49 code of the country, a standard code used by the United Nations</li><li>weighting: aspect based on which the CFs were weighted (only concerns globally aggregated CFs)</li></ul><p> </p><p><strong>Data quality</strong></p><ul><li>original: original estimate (for globally aggregated CFs: mostly original estimates, proxies only considered in areas with current land use)</li><li>proxy_intensity: intensity level was missing; CF was derived from another CF of the same ecoregion and land use type but different intensity level and scaled to the right intensity level</li><li>proxy_type: land use type was missing; CF was derived from the average regional CFs for light use in the same biome and the ecoregion-specific GEP and scaled to the right intensity level if needed</li><li>proxy_gep: global extinction probability (GEP) was missing (only concerns CFs for global relative species loss); GEP estimated based on average GEP per area unit in the same biome and the ecoregion area</li><li>proxy_partial: some species groups were missing but not all (only concerns taxonomically aggregated CFs); aggregation done based on partly original estimates and partly proxies</li><li>proxy_neighbours: country was missing (only concerns country-aggregated CFs); values were estimated based on the average of the three nearest neighbouring countries</li><li>proxy: proxies were considered even in areas without current land use (only concerns globally aggregated CFs)</li></ul><p>Note: proxies in country-aggregated CFs apply to at least one of the ecoregions overlapping with the country and not necessarily all ecoregions</p><p> </p><p><strong>Land use type and intensity data</strong></p><p>Raster file: land_use.tif</p><p>Spatial resolution: 0.08333333, 0.08333333 (x, y)</p><p>Spatial extent: -180, 180, -90, 90 (xmin, xmax, ymin, ymax)</p><p>Coordinate reference system: WGS 84 (EPSG:4326)</p><p> </p><p>Codes</p><ol><li>Primary_vegetation_Minimal (incl. sparse/no vegetation)</li><li>Cropland_Intense</li><li>Cropland_Light</li><li>Cropland_Minimal</li><li>Managed_forest_Intense</li><li>Managed_forest_Light</li><li>Managed_forest_Minimal</li><li>Pasture_Intense</li><li>Pasture_Light</li><li>Pasture_Minimal</li><li>Plantation_Intense</li><li>Plantation_Light</li><li>Plantation_Minimal</li><li>Urban_Intense</li><li>Urban_Light</li><li>Urban_Minimal</li></ol>
Greenhouse gas dynamics in river networks fragmented by drying and damming
<p>River fragmentation by drying and damming is occurring more frequently in the Anthropocene, yet there is limited research about its effects on greenhouse gas (GHG) fluxes. These fragmented rivers have the potential to be important sources of GHGs to the atmosphere through both similar and dissimilar mechanisms. Our objective was to review the literature of the individual and the interactive effects of fragmentation by drying and damming on GHG fluxes in river networks, identifying the magnitudes and drivers of CO<sub>2</sub>, methane (CH<sub>4</sub>), and N<sub>2</sub>O flux rates. We conducted a systematic search of studies addressing the separate and interactive effects of drying and damming on GHG fluxes from running waters. The search was primarily conducted using Web of Science for studies published from 1900 to January 2021. For the 42 studies about rivers impacted by drying, 54 about damming, and 6 about their interactive effects, we collected a suite of qualitative and quantitative information. The major proximal drivers of GHG emissions in river networks impacted by drying were sediment moisture, sediment temperature, sediment organic matter content and sediment texture. In networks impacted by damming, the major proximal drivers were water temperature, dissolved oxygen, and chlorophyll ɑ. We found research lacking in non-arid climates for drying, and on small water retention structures for damming. We propose a conceptual model where the spatial distribution of fragmentation is the principle driver of GHG fluxes at the network scale. </p>
Overcoming confusion and stigma in habitat fragmentation research - supplementary data and R code
<p>Data and code necessary to produce results and figures for the manuscript:</p> <p>Riva, Koper and Fahrig (2024). "Overcoming confusion and stigma in habitat fragmentation research". Biol Rev. Accepted conditional on minor revisions. </p>
Organizing the fragmented landscape of multidisciplinary product development: A mapping of approaches, processes, methods and tools from the scientific literature - Searchable cartographies
<p>This document gathers cartographies for the development of mechatronic products, cyber-physical systems and smart products. The three cartographies presented are associated with an open-access article – see the citation box below – and differ from the ones provided in the article in that they are searchable, which makes it easier to pinpoint references, concepts and techniques. This document comprises a legend, the cartographies and a list of associated references. </p> <p>To contextualize the cartographies, the integration of digital and connectivity technologies in new products can invite companies to adapt their development. Organizing the fragmented landscape of multidisciplinary product development to help companies navigate the dense scientific literature corpus is a first step in supporting them in doing so. Multidisciplinary product development can be investigated by analyzing specific types of products that deal with both software and hardware development and can be referred to as cyber-physical systems, mechatronics, and smart products and systems in the literature. To support their development, 236 “concepts and techniques” (an expression that encompasses approaches, processes, methods and tools) were identified from 167 scientific papers through an extensive literature review and organized based on a four-level model paired with a decision tree. The mapping of the sorted concepts and techniques made it possible to generate graphical representations called “cartographies.” These cartographies represent a database of concepts and techniques for multidisciplinary product development and serve to support companies in their transformation from the product development perspective by providing them with a general overview of the related literature.</p>
Text-fig. 2. Ferns, Ginkgo, and taxodioid conifers. a: Filicalean fern type 1. UAPC-ALTA S sn. b, c: Filicalean fern type 2. b: Overview of specimen, UAPC-ALTA S 59515. c: Detail of (b) to show pinnule shape. d: Azolla primaeva, small plant fragments and rhizoids, BBM-PAL-P000002. e: Metasequoia occidentalis twig with leafy branchlets, BBM- PAL-P000003. f: Ginkgo biloba leaf showing dichotomous venation, GSC 7567. g: Taxodioid branches with flared shoot apices that may represent small cones, UAPC-ALTA S 25090. h: Metasequoia occidentalis branchlet showing opposite leaves, UAPC-ALTA S 59495. i: Taxodioid branchlet showing variation, BBM-PAL-P000004. j: Taxodioid pollen cone, BBM-PAL-P000045. k: Metasequoia seed cone, BBM-PAL-P000005 A. l: cf. Chamaecyparis, BBM-PAL-P000006. Scale bars: a–c, f–l = 1 cm, d = 0.5 cm, e = 2 cm. in The Early Eocene Flora Of Horsefly, British Columbia, Canada And Its Phytogeographic Significance
Text-fig. 2. Ferns, Ginkgo, and taxodioid conifers. a: Filicalean fern type 1. UAPC-ALTA S sn. b, c: Filicalean fern type 2. b: Overview of specimen, UAPC-ALTA S 59515. c: Detail of (b) to show pinnule shape. d: Azolla primaeva, small plant fragments and rhizoids, BBM-PAL-P000002. e: Metasequoia occidentalis twig with leafy branchlets, BBM- PAL-P000003. f: Ginkgo biloba leaf showing dichotomous venation, GSC 7567. g: Taxodioid branches with flared shoot apices that may represent small cones, UAPC-ALTA S 25090. h: Metasequoia occidentalis branchlet showing opposite leaves, UAPC-ALTA S 59495. i: Taxodioid branchlet showing variation, BBM-PAL-P000004. j: Taxodioid pollen cone, BBM-PAL-P000045. k: Metasequoia seed cone, BBM-PAL-P000005 A. l: cf. Chamaecyparis, BBM-PAL-P000006. Scale bars: a–c, f–l = 1 cm, d = 0.5 cm, e = 2 cm.
Text-fig. 4. Monocots. a, b: Large monocot leaf part and counterpart, UAPC-ALTA S 17955A, B. a: Wide leaf showing entire margin at left. b: Counterpart showing dark wide midrib, and and secondaries parallel to one another, arising at low acute angle. c–e: Monocot leaf with parallel venation. c: Overview of elongate monocot leaf with parallel veins horizontal and linear to oval structures and smaller leaf fragment of same type lacking them (at lower right), UAPC-ALTA S 59491. d: Higher magnification of the smaller fragment with weak cross veins. e: Higher magnification of larger specimen with linear to oval structures between parallel veins. f, g: Monocot leaf with parallel venation. Fig. (f) shows higher magnification and (g) shows overview, BBM-PAL-P000009. Scale bars: a, b = 5 cm, c = 4 cm, d–f = 1 cm, g = 2 cm. in The Early Eocene Flora Of Horsefly, British Columbia, Canada And Its Phytogeographic Significance
Text-fig. 4. Monocots. a, b: Large monocot leaf part and counterpart, UAPC-ALTA S 17955A, B. a: Wide leaf showing entire margin at left. b: Counterpart showing dark wide midrib, and and secondaries parallel to one another, arising at low acute angle. c–e: Monocot leaf with parallel venation. c: Overview of elongate monocot leaf with parallel veins horizontal and linear to oval structures and smaller leaf fragment of same type lacking them (at lower right), UAPC-ALTA S 59491. d: Higher magnification of the smaller fragment with weak cross veins. e: Higher magnification of larger specimen with linear to oval structures between parallel veins. f, g: Monocot leaf with parallel venation. Fig. (f) shows higher magnification and (g) shows overview, BBM-PAL-P000009. Scale bars: a, b = 5 cm, c = 4 cm, d–f = 1 cm, g = 2 cm.
Oracle bone fragment image dataset
<p>Oracle bone inscriptions record valuable information about ancient Chinese history in Shang Dynasty. However, many complete oracle bones have broken into pieces over the years, resulting in disjointing sentences. This poses a serious challenge in rejoining oracle bone fragments and interpreting the inscriptions. With oracle bone fragments scattered around the world, researchers have turned their attention to artificial intelligence algorithms to piece together oracle bone fragment images. This paper presents a benchmark datasets for rejoining oracle bone fragment. The benchmark consists of three parts. The first part consists of 5374 high-resolution oracle bone fragment images. The second part consists of 110 oracle bone fragment image pairs that can be rejoined together, including the rejoinable location. The third part consists of the images of oracle bone pairs with matching shapes from multiple sources, divided into two categories: 23,072 rejoinable images and 116003 unrejoinable images. The dataset can be used to evaluate the performance oracle bone rejoining algorithms, the dataset also contains undiscovered rejoinable fragments, which offer promising opportunities for further analysis. </p>
Raw diffraction images of the crystal structure of human VISTA extra cellular domain in complex with Fab fragment of pH-selective anti-VISTA antibody
<p>The diffraction datasets was collected at X10SA, SLS. The dataset was collected from one crystal using a rotation scheme for 222º oscillation with the following experimental parameters; Wavelength: 0.9998 Å, Detector: EIGER2 Si 16M (DECTRIS Co. Ltd.). The crystal belonged to space group C 1 2 1 with unit cell parameters a=207.66, b=39.51, c=177.98 Å, and beta=117.12°.</p><p>PDB ID: 8TBQ</p>
Fig. 2 in Reproductive success of Trypoxylon (Trypargilum) lactitarse (Hymenoptera: Crabronidae) in a fragmented landscape
Fig. 2. StUdY areas in the mUnicipalities of Goianápolis, Leopoldo de BUlhões, BonfinÓpolis, Anápolis, and Hidrolânida, state of Goiás, Brazil.
Fig. 7 in Reproductive success of Trypoxylon (Trypargilum) lactitarse (Hymenoptera: Crabronidae) in a fragmented landscape
Fig. 7. Results of the Generalized Linear Mixed Model (GLMM) between parasitoidism rate (proportion of nests attacked by parasitoids) and fragment size. No difference was foUnd between small and large fragments for parasitoidism rate (z = 0.394, p = 0.694).
Fig. 6 in Reproductive success of Trypoxylon (Trypargilum) lactitarse (Hymenoptera: Crabronidae) in a fragmented landscape
Fig. 6. Results of the Generalized Linear Mixed Model (GLMM) between the numbers of adults emerged from each nest and fragment size. No difference was foUnd between small and large fragments for sUrvival rate (z = 0.784, p = 0.433).
Fig. 1 in Reproductive success of Trypoxylon (Trypargilum) lactitarse (Hymenoptera: Crabronidae) in a fragmented landscape
Fig. 1. Specimen of Trypoxylon (Trypargilum) lactitarse Saussure, 1867 (HYmenoptera: Crabronidae) captured in Goianápolis, state of Goiás, Brazil.
Fig. 5 in Reproductive success of Trypoxylon (Trypargilum) lactitarse (Hymenoptera: Crabronidae) in a fragmented landscape
Fig. 5. Results of the Generalized Linear Model (GLM) between fragment size and the average nUmber of cells per fragment. No difference was found between small and large fragments for mean number of nest cells (z = 1.155, p = 0.248).
Figure 3 in Conservation of Manilkara salzmannii in an Atlantic Forest fragment through the study of fruits and seeds
Figure 3. Absolute frequency distribution of the biometric characteristics of the fruits and seeds of Manilkara salzmannii.
Figura 2. Manilkara salzmannii. A in Conservation of Manilkara salzmannii in an Atlantic Forest fragment through the study of fruits and seeds
Figura 2. Manilkara salzmannii. A. tree in natural forest; B. mature fruits collected; C. mature fruit size (scale = 3cm); D. seed size (scale = 1 cm).
Fig. 4 in Reproductive success of Trypoxylon (Trypargilum) lactitarse (Hymenoptera: Crabronidae) in a fragmented landscape
Fig. 4. Results of the Generalized Linear Model (GLM) between the nests foundation rate and the log of fragment area. The foundation rate increasing with increasing fragment area (z = 8.189, p <0.001).
Figure 1 in Conservation of Manilkara salzmannii in an Atlantic Forest fragment through the study of fruits and seeds
Figure 1. Geographical location of the study area. A. Municipality of Macaíba, RN, Brazil; B. fragment with a population of Manilkara salzmannii.
Fig. 9 in Reproductive success of Trypoxylon (Trypargilum) lactitarse (Hymenoptera: Crabronidae) in a fragmented landscape
Fig. 9. Results of the Generalized Linear Mixed Model (GLMM) between sex ratios and fragment size category. Despite a tendency for a biased sex ratio in favor females in small areas, the difference was not significant (z = 1.224, p = 0.221).
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.