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2,721 results for “Connectivity”
Fig. 2. Atritomus vicinus Grouvelle, 1908 in West Palaearctic taxa formerly connected to the 'old' genus Atritomus Reitter, 1877 (Coleoptera, Mycetophagidae): taxonomy, distribution, and description of a new genus
Fig. 2. Atritomus vicinus Grouvelle, 1908 (= Typhaeola vicina (Grouvelle, 1908) comb. nov.), holotype (EC12477). A. Habitus, dorsal view. B. Habitus, lateral view. C. Habitus, ventral view. D. Labels. Scale bar = 1 mm.
Fig. 1 in West Palaearctic taxa formerly connected to the 'old' genus Atritomus Reitter, 1877 (Coleoptera, Mycetophagidae): taxonomy, distribution, and description of a new genus
Fig. 1. Stereophilus filicornis (Reitter, 1887) gen. et comb. nov., ♂ from Italy, Tuscany. A. Habitus. B. Aedeagus (dorsal view). C. Aedeagus (lateral view). D. Right hind wing of a male from France, Haute-Garonne. Scale bars: A, D = 1 mm; B–C = 0.5 mm.
Brain functional connectivity data in anesthetized participants and patients with neuropathological or psychiatric diagnoses
<p>Five fMRI datasets were collected from independent research sites including: propofol deep sedation (PDS; drug effect site concentration= ~2.4 μg/ml) in Dataset-1, propofol general anesthesia (PGA; drug effect site concentration= 4.0 μg/ml) in Dataset-2, ketamine anesthesia (KA) in Dataset-3, unresponsive wakefulness syndrome (UWS) in Dataset-4, and schizophrenia (SCHZ), bipolar disorder (BD), and attentional deficit hyperactivity disorder (ADHD) in Dataset-5. Following fMRI data preprocessing, the fMRI time courses were extracted from 400 cortical areas according to a well-established brain parcellation scheme (Schaefer's 400 ROIs). A connectivity matrix was then calculated using Pearson correlation resulting in a 400x400 connectivity matrix for each participant and each condition.</p>
Making connections: Coding, weaving and flamenco
<p>As part of the city-wide Sheffield Showcase weekend across the city in September 2021, live coding musician Lucy Cheesman, researcher/choreographer Rosa Cisneros and researcher/musician Alex McLean collaborated on a drop-in family workshop held at Pitsmoor Scout Hut in Sheffield UK. We explored different kinds of patterns with participants, focussing on the underlying algorithmic structures. In particular we explored weaving patterns using a semi-automated handloom driven by a simple programming language, sonic patterns using the live coding environment TidalCycles, and flamenco patterns using notations and of course our bodies! Film produced by Reel Master Production - https://www.reelmasterproduction.uk This workshop was funded as part of Sheffield Showcase with support from the University of Sheffield and Sheffield City Council, and a UKRI funded 'making connections' grant awarded via the UK Science Festivals Network. The film, and Alex McLean's participation was funded as part of the European Research Council's PENELOPE project.</p>
Data to accompany the publication "Combined biophysical and genetic modelling approaches reveal complementary information about population connectivity of New Zealand green-lipped mussels"
<p>Data to accompany the publication "Combined biophysical and genetic modelling approaches reveal complementary information about population connectivity of New Zealand green-lipped mussels". </p> <p>migrationmatrix14.txt contains the particle tracking matrix, with the total number of particles that migrated from row i to column j (out of a total of 2217864 particles released per population).</p> <p>mussel_microsat_Genepop.txt contains the microsatellite data for each population in Genepop format.</p>
Larval dispersal patterns and connectivity of Acropora on Florida's Coral Reef and its implications for restoration
Since the 1980s, populations of Acropora cervicornis and A. palmata have experienced severe declines due to disease and anthropogenic stressors; resulting in their listing as threatened, and their need for restoration. In this study, larval survival and competency data were collected and used to calibrate a very high-resolution hydrodynamic model (up to 100m) to determine the dispersal patterns of Acropora species along the Florida's Coral Reef. The resulting connectivity matrices was incorporated into a metapopulation model to compare strategies for restoring Acropora populations. This study found that Florida's Coral Reef was historically a well-connected system, and that spatially selective restoration may be able to stimulate natural recovery. Acropora larvae are predominantly transported northward along the Florida's Coral Reef, however southward transport also occurs, driven by tides and baroclinic eddies. Local retention and self-recruitment processes were strong for a broadcast spawner with a long pelagic larval duration. Model simulations demonstrate that it is beneficial to spread restoration effort across more reefs, rather than focusing on a few reefs. Differences in population patchiness between the Acropora cervicornis and A. palmata drive the need for different approaches to their management plans. This model can be used as a tool to address the species-specific management to restore genotypically diverse Acropora populations on the Florida's Coral Reef, and its methods could be expanded to other vulnerable populations.
A Parcellation Scheme of Mouse Isocortex Based on Reversals in Connectivity Gradients
<p>This dataset contains a parcellation of mouse isocortex into a hierarchy of subregions. The parcellation is based on the detection of gradient reversals in voxelized connectivity, as described in our latest preprint manuscript.</p> <p>The connectivity dataset used for this purpose was the voxelized mouse connectome published by the Allen Institute for Brain Science, available at <a href="https://connectivity.brain-map.org">connectivity.brain-map.org</a> (data) and <a href="https://github.com/AllenInstitute/mouse_connectivity_models">github.com/AllenInstitute</a> (code)</p> <p>Region id annotations are provided in .nrrd format. Refer to the <a href="http://teem.sourceforge.net/nrrd/format.html">NRRD documentation</a> for details.</p> <p>The hierarchy is provided in the accompanying json file. For each region, an entry "structure_id_path" is provided that lists the ids of regions on the path from the root (i.e. isocortex) to the region in question.</p> <p>For more information, please read the accompanying manuscript. We will provide the link here, once it is online.</p>
Over the hills and through the farms: Land use and topography influence genetic connectivity of northern leopard frog (Rana pipiens) in the Prairie Pothole Region
<p><em>Context</em></p> <p>Agricultural land-use conversion has fragmented prairie wetland habitats in the Prairie Pothole Region (PPR), an area with one of the most wetland-dense regions in the world. This fragmentation can lead to negative consequences for wetland obligate organisms, heightening risk of local extinction and reducing evolutionary potential for populations to adapt to changing environments.</p> <p><em>Objectives</em></p> <p>This study models biotic connectivity of prairie-pothole wetlands using landscape genetic analyses of the northern leopard frog (<em>Rana pipiens</em>) to: (1) identify population structure and (2) determine landscape factors driving genetic differentiation and possibly leading to population fragmentation.</p> <p><em>Methods</em></p> <p>Frogs from 22 sites in the James River and Lake Oahe river basins in North Dakota were genotyped using Best-RAD sequencing at 2868 bi-allelic single nucleotide polymorphisms (SNPs). Population structure was assessed using STRUCTURE, DAPC, and fineSTRUCTURE. Circuitscape was used to model resistance values for ten landscape variables that could affect habitat connectivity.</p> <p><em>Results</em></p> <p>STRUCTURE results suggested a panmictic population, but other more sensitive clustering methods identified six spatially organized clusters. Circuit theory-based landscape resistance analysis suggested land use, including cultivated crop agriculture, and topography were the primary influences on genetic differentiation.</p> <p><em>Conclusions</em></p> <p>While the <em>R. pipiens</em> populations appear to have high gene flow, we found a difference in the patterns of connectivity between the eastern portion of our study area which was dominated by cultivated crop agriculture, versus the western portion where topographic roughness played a greater role. This information can help identify amphibian dispersal corridors and prioritize lands for conservation or restoration.</p>
BeeDNA: microfluidic environmental DNA metabarcoding as a tool for connecting plant and pollinator communities
<p><strong>Data repository accompanying the paper 'BeeDNA: microfluidic environmental DNA metabarcoding as a tool for connecting plant and pollinator communities' by Harper et al. (2021).</strong></p> <p><br> <strong>1_Raw_Data.zip</strong><br> This zipped folder contains the raw sequence data (sorted by primer set and demultiplexed) for both sequencing runs (2019-10-24 and 2019-11-11). To decompress each file, run: </p> <pre><code>tar -xvf filename.bz2</code></pre> <p>This will create a folder for each primer set containing the raw reads for each sample/control.</p> <p><br> <strong>2_Anacapa_Bioinformatic_Processing.zip</strong></p> <p>This zipped folder contains all files needed to perform bioinformatic processing with Anacapa. Please process sequence data belonging to each primer set individually (i.e. do not process sequence data belonging to different primer sets together).</p> <p><br> <strong>3_metaBEAT_Bioinformatic_Processing.zip </strong></p> <p>This zipped folder contains the scripts and files needed to perform bioinformatic processing with metaBEAT. Before running the scripts, move the raw reads for each sample belonging to each primer set into the dedicated folder within metaBEAT_Bioinformatic_Processing, e.g. all .fastq files in Raw_Data > BF1_BR1 should be moved to metaBEAT_Bioinformatic_Processing > BF1-BR1 > raw_reads.</p> <p>To run metaBEAT, you will have to install Docker on your computer. Docker is compatible with all major operating systems, but see the Docker documentation for details. On Ubuntu, installing Docker should be as easy as:</p> <pre><code>sudo apt-get install docker.io</code></pre> <p>Once Docker is installed, you can enter the environment by typing:</p> <pre><code>sudo docker run -i -t --net=host --name metaBEAT -v $(pwd):/home/working chrishah/metabeat /bin/bash</code></pre> <p>This will download the metaBEAT image (if not yet present on your computer) and enter the 'container', i.e. the self contained environment (NB: sudo may be necessary in some cases). With the above command, the container's directory /home/working will be mounted to your current working directory (as instructed by $(pwd)). In other words, anything you do in the container's /home/working directory will be synced with your current working directory on your local machine.</p> <p>Please process sequence data belonging to each primer set individually (i.e. do not process sequence data belonging to different primer sets together). An example of expected outputs can be seen in the Jupyter Notebook for the BF1/BR1 primer set from the 2019-11-11 sequencing run.</p> <p><br> <strong>4_Illinois_Invert_Reference_Database.zip</strong></p> <p>This zipped folder contains all files that were used to generate the custom COI and 16S reference databases for invertebrates that occur in Illinois, U.S. You will need to have metaBEAT installed (see above) before you try to run any Jupyter Notebooks (.ipynb files).</p> <p><br> <strong>5_ecoPCR.zip</strong></p> <p>This zipped folder contains all files used to perform ecoPCR for each primer set evaluated for microfluidic eDNA metabarcoding. You will need to <a href="https://git.metabarcoding.org/obitools/ecopcr/wikis/home">install ecoPCR</a> before running any shell scripts.</p> <p><br> <strong>6_Tidied_Data.zip</strong></p> <p>This zipped folder contains the taxonomically assigned data for both sequencing runs produced by metaBEAT and Anacapa. These were copied over from the folders 2_Anacapa_Bioinformatic_Processing and 3_metaBEAT_Bioinformatic_Processing and rearranged into a more logical order. These files are used as the input for data analysis using R.</p> <p><br> <strong>7_Data_Analysis.zip</strong></p> <p>This zipped folder contains all scripts and metadata required to summarise and statistically analyse data in R.</p> <p> </p> <p><strong>Please contact Dr Lynsey Harper (lynsey.harper2@gmail.com) or Dr Mark Davis (davis63@illinois.edu) if you encounter any issues!</strong></p>
Hypothetical landscapes to evaluate connectivity metrics of protected area networks.
<p>This repo contains the raw datasets (as GIS shapefiles) useful to evaluate connectivity metrics of protected area networks. Please suggest if additional landscapes could be added that would be useful to evaluate an additional class or characteristic of protected area networks. They were created using Google Earth Engine script: <strong><a href="https://code.earthengine.google.com/d1a8dfa3202ac8b4e55657bd3b5a1160">https://code.earthengine.google.com/d1a8dfa3202ac8b4e55657bd3b5a1160</a>.</strong></p> <p>Two shapefiles are provided: (1) ProNet_connectivity_library_L1_26pa -- this contains polygons that represent the size and shape of protected areas (PAs); (2) ProNet_connectivity_library_L1_26pae -- this contains polylines that represent "edges" that do not represent any protected area but denotes that two PAs are connected. Note that these landscapes are fictitious, and represented at the global origin (i.e. 0.0 degrees latitude and 0.0 degrees longitude) -- and are quite small so zooming in will be required to see them in GIS software.</p>
Connective Field maps via reverse-correlation
<p>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.</p> <p>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.</p> <p>You will need SamSrf to read these files (version 9.3 recommended). It has been tested on Matlab R2020a & b.</p> <p> </p>
Text-fig. 14. Dendrograms of studied taxa based on enamel ultrastructure characters. a: dendrogram based on all three enamel types; b: dendrogram based on enamel type I. in The Ultrastructure Of The Tooth Enamel Of Small Equus Of The "Tarpan" Group And Their Possible Phylogenetic Connections
Text-fig. 14. Dendrograms of studied taxa based on enamel ultrastructure characters. a: dendrogram based on all three enamel types; b: dendrogram based on enamel type I.
Text-fig. 13. Enamel ultrastructure of I1, Equus hydruntinus (Kabazi 2). a: vertical sections, scale bar = 100 Μm; b: horizontal and vertical arrangement of prisms in the HSB structure, scale bar = 10 Μm; c: unstructured PLEX enamel at the end of the root, scale bar = 100 Μm. in The Ultrastructure Of The Tooth Enamel Of Small Equus Of The "Tarpan" Group And Their Possible Phylogenetic Connections
Text-fig. 13. Enamel ultrastructure of I1, Equus hydruntinus (Kabazi 2). a: vertical sections, scale bar = 100 Μm; b: horizontal and vertical arrangement of prisms in the HSB structure, scale bar = 10 Μm; c: unstructured PLEX enamel at the end of the root, scale bar = 100 Μm.
Text-fig. 12. HSB of first and second upper incisors of Equus przewalskii (Chornobyl Exclusion Zone). a, b: vertical section, scale bar = 100 Μm; c: horizontal cross-section, scale bar = 50 Μm. in The Ultrastructure Of The Tooth Enamel Of Small Equus Of The "Tarpan" Group And Their Possible Phylogenetic Connections
Text-fig. 12. HSB of first and second upper incisors of Equus przewalskii (Chornobyl Exclusion Zone). a, b: vertical section, scale bar = 100 Μm; c: horizontal cross-section, scale bar = 50 Μm.
Text-fig. 9. Enamel ultrastructure of M1-2, Equus hydruntinus (Kabazi 2). a, b: type I; c, d: type II. in The Ultrastructure Of The Tooth Enamel Of Small Equus Of The "Tarpan" Group And Their Possible Phylogenetic Connections
Text-fig. 9. Enamel ultrastructure of M1-2, Equus hydruntinus (Kabazi 2). a, b: type I; c, d: type II.
Text-fig. 8. Enamel ultrastructure of M1, Equus przewalskii (Chornobyl Exclusion Zone). a, b: enamel row, scale bar = 100 and 20 Μm respectively; c: type I and III, scale bar = 20 Μm; d–f: first type enamel arrangement, scale bar d = 10, e = 3 Μm and f = 2 Μm; g, h: prisms of TZ, scale bar = 50 and 30 Μm respectively; i: type II near OES, scale bar = 20 Μm. in The Ultrastructure Of The Tooth Enamel Of Small Equus Of The "Tarpan" Group And Their Possible Phylogenetic Connections
Text-fig. 8. Enamel ultrastructure of M1, Equus przewalskii (Chornobyl Exclusion Zone). a, b: enamel row, scale bar = 100 and 20 Μm respectively; c: type I and III, scale bar = 20 Μm; d–f: first type enamel arrangement, scale bar d = 10, e = 3 Μm and f = 2 Μm; g, h: prisms of TZ, scale bar = 50 and 30 Μm respectively; i: type II near OES, scale bar = 20 Μm.
Text-fig. 4. Enamel ultrastructure of M1-2, Equus gmelini (Myrne). a: enamel row, scale bar = 30 Μm; b: type I and III, scale bar = 20 Μm; c: type II near OES border, scale bar = 2 Μm. in The Ultrastructure Of The Tooth Enamel Of Small Equus Of The "Tarpan" Group And Their Possible Phylogenetic Connections
Text-fig. 4. Enamel ultrastructure of M1-2, Equus gmelini (Myrne). a: enamel row, scale bar = 30 Μm; b: type I and III, scale bar = 20 Μm; c: type II near OES border, scale bar = 2 Μm.
Text-fig. 10. Enamel ultrastructure of I1 (a) and I2 (b, c), Equus gmelini, tarpan (Myrne). a: vertical section; b, c: horizontal cross-section, scale bar = 250 and 100 Μm respectively. in The Ultrastructure Of The Tooth Enamel Of Small Equus Of The "Tarpan" Group And Their Possible Phylogenetic Connections
Text-fig. 10. Enamel ultrastructure of I1 (a) and I2 (b, c), Equus gmelini, tarpan (Myrne). a: vertical section; b, c: horizontal cross-section, scale bar = 250 and 100 Μm respectively.
Text-fig. 5. Enamel ultrastructure of m1-2, Equus gmelini (Kamiana Mohyla). a: type I, scale bar = 2 Μm; b: type II, scale bar = 10 Μm; c: type II near the OES border, scale bar = 2 Μm. in The Ultrastructure Of The Tooth Enamel Of Small Equus Of The "Tarpan" Group And Their Possible Phylogenetic Connections
Text-fig. 5. Enamel ultrastructure of m1-2, Equus gmelini (Kamiana Mohyla). a: type I, scale bar = 2 Μm; b: type II, scale bar = 10 Μm; c: type II near the OES border, scale bar = 2 Μm.
Text-fig. 6. Enamel ultrastructure of M2, Equus gmelini (Hirzhevo). a: type I and III, scale bar = 20 Μm; b: IPM and PE first type prisms, scale bar = 3 Μm; c, d: wavy/decussated enamel of TZ, scale bar = 20 and 10 Μm respectively; e: type II near OES border, scale bar = 2 Μm; f: type III, scale bar = 2 Μm. in The Ultrastructure Of The Tooth Enamel Of Small Equus Of The "Tarpan" Group And Their Possible Phylogenetic Connections
Text-fig. 6. Enamel ultrastructure of M2, Equus gmelini (Hirzhevo). a: type I and III, scale bar = 20 Μm; b: IPM and PE first type prisms, scale bar = 3 Μm; c, d: wavy/decussated enamel of TZ, scale bar = 20 and 10 Μm respectively; e: type II near OES border, scale bar = 2 Μm; f: type III, scale bar = 2 Μm.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.