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148 results for “regional integration”
CCE LTER process cruise, in the California Current region, event log records including date, time, position and activity for use in post-cruise data integration based on co-sampling indexes. From 2006 to 2019 CCE LTER used a locally developed event logging system. During P2107, CCE LTER started to utilize the R2R Event Logger on UNOL ships, 2006 - 2024 (ongoing).
The event logger program developed and maintained by the California Cooperative Oceanic Fisheries Investigations, SIO, program is used aboard CCE LTER process cruises to create indexes with temporal, spatial and activity information for post-cruise data integration. The event log is configured aboard the ship for the recording of sampling events by both ship crew personnel on the bridge, and research personnel in the lab. The event log is processed post-cruise to correct for various errors.
MigParl. A Corpus of Speeches on Migration and Integration in Germany's Regional Parliaments
<p>MigParl is an indexed and linguistically annotated corpus of speeches on migration and integration affairs in Germany’s regional parliaments (“Landtage”). The corpus has been prepared in the MigTex Project (principal investigators: Andreas Blätte / University of Duisburg-Essen, Ruud Koopmans / Berlin Social Science Center), using the resources and the infrastructure of the <a href="http://polmine.github.io">PolMine Project</a>.</p> <p>MigTex was part of a larger joint project to establish the research community of the <em>German Centre for Migration and Integration Affairs</em> (<em>Deutsches Zentrum für Migration and Integrationsforschung</em> / DeZIM). Funding awarded by Germany’s <em>Federal Ministry for Family Affairs, Senior Citizens, Women and Youth</em> (<em>Bundesministerium für Familie, Senioren, Frauen und Jugend</em> / BMFSFJ) is gratefully acknowleged.</p>
Fig. 2 in Integrative taxonomy reveals two new cryptic species of Hyphessobrycon Durbin, 1908 (Teleostei: Characidae) from the Maracaçumé and middle Tocantins River basins, Eastern Amazon region
Fig. 2. Hyphessobrycon frickei Guimarães, Brito, Bragança, Katz & Ottoni sp. nov. (CICCAA 02388), 17.7 mm SL; jaw suspensory. A. Premaxillary. B. Maxilla. C. Dentary. Scale bar: 1 mm
Fig. 8 in Integrative taxonomy reveals two new cryptic species of Hyphessobrycon Durbin, 1908 (Teleostei: Characidae) from the Maracaçumé and middle Tocantins River basins, Eastern Amazon region
Fig. 8. Topology of the ultrametric tree performed in BEAST ver. 1.8.4 including unique haplotypes summarizing the results of GMYC, bPTP and ABGD. Numbers above and below branches are posterior probability values. The star indicates the Hyphessobrycon copelandi clade.
Data from: harnessing the power of regional baselines for broad-scale genetic stock identification: a multistage, integrated, and cost-effective approach
<p>In mixed-stock fishery analyses, genetic stock identification (GSI) estimates the contribution of each population to a mixture and is typically conducted at a regional scale using genetic baselines specific to the stocks expected in that region. Often these regional baselines cannot be combined to produce broader geographical baselines due to non-overlapping populations and genetic markers. In cases where the mixture contains stocks spanning across a wide area, a broad-scale baseline is created, but often at the cost of resolution. Here, we introduce a new GSI method to harness the resolution capabilities of baselines developed for regional applications in the analysis of mixtures containing individuals from a broad geographic range. This method employs a multistage framework that allows disparate baselines to be used in a single integrated process that produces estimates along with the propagated errors from each stage. All individuals in the mixture sample are required to be genotyped for all genetic markers in the baselines used by this model, but the baselines do not require overlap in genetic markers or populations representing the broad-scale or regional baselines.</p> <p>We demonstrate our integrated multistage GSI model using a synthesized data set made up of Chinook salmon, <em>Oncorhynchus tshawytscha</em>, from the North Bering Sea of Alaska. The data set is designed to be run using R package, Ms.GSI, and it does not represent the composition of the real fishery. The results show an improved accuracy for estimates using an integrated multistage framework, compared to the conventional framework of using separate hierarchical steps. The integrated multistage framework allows GSI of a wide geographic area without first developing a large scale, high-resolution genetic baseline or dividing a mixture sample into smaller regions beforehand. This approach is more cost-effective than updating range-wide baselines with all regionally important markers.</p>
Figure 5 in An integrative description of Mesobiotus ethiopicus sp. nov. (Tardigrada: Eutardigrada: Parachela: Macrobiotidae: harmsworthi group) from the northern Afrotropic region
Figure 5. Mesobiotus ethiopicus sp. nov. – PCM images of the midsection of various types of egg processes. Scale bars in µm.
Figure 6 in An integrative description of Mesobiotus ethiopicus sp. nov. (Tardigrada: Eutardigrada: Parachela: Macrobiotidae: harmsworthi group) from the northern Afrotropic region
Figure 6. Mesobiotus ethiopicus sp. nov. – SEM images of eggs: A–B – entire view of 2 different eggs; C–D – egg surface between processes; E–F – egg processes; G–H – a top part of the processes terminated by several short flexible filaments. Filled indented arrowheads indicate poorly developed connection between the processes, filled flat arrowheads indicate rare fully developed connections between processes. Scale bars in µm.
Figure 2 in An integrative description of Mesobiotus ethiopicus sp. nov. (Tardigrada: Eutardigrada: Parachela: Macrobiotidae: harmsworthi group) from the northern Afrotropic region
Figure 2. Mesobiotus ethiopicus sp. nov. – PCM images of the buccal apparatus: A – an entire buccal apparatus (paratype); B–C – the oral cavity armature of the holotype, dorsal and ventral teeth respectively; D–E – the oral cavity armature of the paratype, dorsal and ventral teeth respectively; F–G – the oral cavity armature of the paratype, dorsal and ventral teeth respectively; H–I – placoid morphology of the paratype, ventral and dorsal placoids, respectively. Filled flat arrowheads indicate the larger teeth in the second band of teeth, arrows indicate the medioventral tooth in the third band of teeth, empty flat arrowheads indicate subterminal constrictions in the third macroplacoid. Scale bars in µm.
Figure 4 in An integrative description of Mesobiotus ethiopicus sp. nov. (Tardigrada: Eutardigrada: Parachela: Macrobiotidae: harmsworthi group) from the northern Afrotropic region
Figure 4. Mesobiotus ethiopicus sp. nov. – PCM images of the eggs: A – midsection; B – surface; C–D – surfaces under 1000× magnification. Asterisk indicates the wrinkled surface inside the areolae, filled indented arrowheads indicate poorly developed connection between the processes, filled flat arrowheads indicate rare fully developed connections between processes. Scale bars in µm.
Figure 1 in An integrative description of Mesobiotus ethiopicus sp. nov. (Tardigrada: Eutardigrada: Parachela: Macrobiotidae: harmsworthi group) from the northern Afrotropic region
Figure 1. Mesobiotus ethiopicus sp. nov. – PCM image of habitus: A – dorsoventral projection (holotype). Scale bar in µm.
Figure 3 in An integrative description of Mesobiotus ethiopicus sp. nov. (Tardigrada: Eutardigrada: Parachela: Macrobiotidae: harmsworthi group) from the northern Afrotropic region
Figure 3. Mesobiotus ethiopicus sp. nov. – PCM images of claws: A – claws I with smooth lunules; B – claws II with smooth lunules; C – claws IV with moderately serrated lunules. All claw photos from the holotype. Arrows indicate the cuticular bars, the filled arrowhead indicates the horseshoe structure connecting the anterior and the posterior claw. Scale bars in µm.
Fig 4 in Tinnea gombea (Lamiaceae), a new species from the Sudanian savanna region, Nigeria based on integrative evidence
Fig 4. Scanned type herbarium specimens of the most similar Tinnea species. (A) T. galpini; (B) T. aethiopica; (C) T. barteri; and (D) T. gombea to show the similarities and variations in morphological characters. Copyright: the Board of Trustees of the Royal Botanic Gardens, Kew, United Kingdom. Reproduced with the consent of the Royal Botanic Gardens, Kew (K). https://doi.org/10.1371/journal.pone.0280550.g004
Fig 3 in Tinnea gombea (Lamiaceae), a new species from the Sudanian savanna region, Nigeria based on integrative evidence
Fig 3. Distribution map of Tinnea gombea (black solid circle). The map was generated using naijR package version 0.4.0 as implemented in R version 4.2.0. and is therefore for illustrative purposes only. https://doi.org/10.1371/journal.pone.0280550.g003
Fig 2 in Tinnea gombea (Lamiaceae), a new species from the Sudanian savanna region, Nigeria based on integrative evidence
Fig 2. Morphological features of Tinnea gombea. (A) Plant habit and habitat; (B–D) young branches bearing vegetative and reproductive features; (E) Older branch with mature leaves, bracts and fruits; (G) closer view of dried fruits and leaves; (H) closer view of the fluffy seed with a tuft of basal hairs. Photos by D.A. Zhigila. https://doi.org/10.1371/journal.pone.0280550.g002
Fig 1. A 50 in Tinnea gombea (Lamiaceae), a new species from the Sudanian savanna region, Nigeria based on integrative evidence
Fig 1. A 50% majority-rule consensus tree of the genus Tinnea obtained from the Bayesian analysis of the combined datasets of the nrITS, and matK, rbcL and trnL-F. Numbers on nodes indicate the posterior probability and the bootstrap support values of>0.80 and>55% respectively. Note that the new species (T. gombea) is in bold blue. https://doi.org/10.1371/journal.pone.0280550.g001
Model fields supporting the publication "Integrated Assessment of the Risks to Ocean Acidification in the Northern High Latitudes: Regional Comparison of Exposure, Sensitivity and Adaptive Capacity of Pelagic Calcifiers"
<p>These are the model outputs supporting the described manuscript. They include monthly averaged output of aragonite saturation state for each year during the 10-year hindcast. Also included is the particle tracking output, for both the Bering Sea and the Gulf of Alaska, as described in the manuscript.</p>
FIG. 5. — Integrated 39 in Mammal bearing late Miocene tuffs of the Akkaşdağı region; distribution, age, petrographical and geochemical characteristics
FIG. 5. — Integrated 39Ar/40Ar ages for two samples from the Akkaşdağı tuffs; A, sample AKK-1 from the main tuff horizon, three meters below the bone pocket AK-5; B, sample AKK-2 from the reworked tuffs filling the bone pocket AK-5.
Data from: harnessing the power of regional baselines for broad-scale genetic stock identification: a multistage, integrated, and cost-effective approach
Open the record for dataset details and reuse information.
Assessing Heavy Metal Contamination in Agricultural Soils: A Predictive Model Integrating GIS Tools and Probability-Risk Matrix – Case Study: Guarda Region, Portugal
<p>In these files we can find the final risk map of heavy metal contamination for the guarding area in Portugal obtained according to the methodology explained in the paper "Assessing Heavy Metal Contamination in Agricultural Soils: A Predictive Model Instegrating GIS Tools and Probability-Risk Matrix - Case Study: Guarda Region (Portugal)</p> <p>Final Risk Equal.tiff: GeoTiff with a pixel size of 30m. EPSG:3763 - ETRS89 / Portugal TM06</p> <p>Also attached is the symbolisation for the image in .qml (Quantum GIS Layer Style File) format.</p> <p>A file called RISK RECLASS is also available, where you can find the risk classification maps for each of the studied factors: </p> <ul> <li>Proximity to roads</li> <li>Proximity to industrial areas</li> <li>Ph</li> <li>Soil organic content</li> <li>Slope</li> <li>Soil texture</li> <li>Mining extraction areas </li> <li>Drainage</li> </ul> <p>finally a DATABASE file where the data of the 360 points for the calculation of the risk maps can be found. </p>
Data from: Genomic integrity of Phyciodes butterfly species in a region of contact (Lepidoptera: Nymphalidae)
<p>Crescent butterflies of the <em>Phyciodes tharos </em>group have a long-standing reputation for taxonomic difficulty.<em> </em>We assessed species boundaries in the <em>P. tharos </em>group using genome-wide SNP data, focusing on sampling in western Canada where four species (<em>P. tharos</em>, <em>P. cocyta</em>, <em>P. pulchella,</em> and <em>P. batesii</em>) have classically been recognized. Phylogenetic and cluster-based analyses confirm that there are indeed four species based on the maintenance of genomic integrity in the presence of occasional hybridization and low levels of gene flow. Mitochondrial COI does not consistently distinguish species, with haplotypes extensively shared between species. Here, we provide data matrices and tree files from the phylogenetic and cluster-based analyses.</p>
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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.