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2,721 results for “CONNECTIVITY”
Figure 20 from: Glover A, Dahlgren T, Taboada S, Paterson G, Wiklund H, Waeschenbach A, Cobley A, Martínez P, Kaiser S, Schnurr S, Khodami S, Raschka U, Kersken D, Stuckas H, Menot L, Bonifacio P, Vanreusel A, Macheriotou L, Cunha M, Hilário A, Rodrigues C, Colaço A, Ribeiro P, Błażewicz M, Gooday A, Jones D, Billett D, Goineau A, Amon D, Smith C, Patel T, McQuaid K, Spickermann R, Brager S (2016) The London Workshop on the Biogeography and Connectivity of the Clarion-Clipperton Zone. Research Ideas and Outcomes 2: e10528. https://doi.org/10.3897/rio.2.e10528
Figure 20 - The species often referred to as Ophiomusium cf. glabrum which is a likely cryptic species complex in CCZ samples, but is present in large numbers and is recognisable (at least to genus) in image surveys. This is a current target taxon for population studies. Image source: (Glover et al. 2016b).
Figure 14 from: Glover A, Dahlgren T, Taboada S, Paterson G, Wiklund H, Waeschenbach A, Cobley A, Martínez P, Kaiser S, Schnurr S, Khodami S, Raschka U, Kersken D, Stuckas H, Menot L, Bonifacio P, Vanreusel A, Macheriotou L, Cunha M, Hilário A, Rodrigues C, Colaço A, Ribeiro P, Błażewicz M, Gooday A, Jones D, Billett D, Goineau A, Amon D, Smith C, Patel T, McQuaid K, Spickermann R, Brager S (2016) The London Workshop on the Biogeography and Connectivity of the Clarion-Clipperton Zone. Research Ideas and Outcomes 2: e10528. https://doi.org/10.3897/rio.2.e10528
Figure 14 - Nematoda of the Clarion-Clipperton Zone, recovered from abyssal sediments during the JPI Oceans 'SO-239' cruise aboard the R/V Sonne in February-March 2015. Image credit: Lara Macheriotou, University of Ghent.
Figure 13 from: Glover A, Dahlgren T, Taboada S, Paterson G, Wiklund H, Waeschenbach A, Cobley A, Martínez P, Kaiser S, Schnurr S, Khodami S, Raschka U, Kersken D, Stuckas H, Menot L, Bonifacio P, Vanreusel A, Macheriotou L, Cunha M, Hilário A, Rodrigues C, Colaço A, Ribeiro P, Błażewicz M, Gooday A, Jones D, Billett D, Goineau A, Amon D, Smith C, Patel T, McQuaid K, Spickermann R, Brager S (2016) The London Workshop on the Biogeography and Connectivity of the Clarion-Clipperton Zone. Research Ideas and Outcomes 2: e10528. https://doi.org/10.3897/rio.2.e10528
Figure 13 - Selected live images of specimens recovered from the UK Seabed Resources Ltd (UKSRL) Abyssal Baseline (ABYSSLINE) research cruise to the eastern Clarion-Clipperton Zone, February-March 2015. Image credit: Adrian Glover, Thomas Dahlgren, Helena Wiklund.
Figure 1 from: Smith C, Todd M, Patiny L, Swain C, Southan C, Williamson A, Clark A (2016) SCINDR - The SCience INtroDuction Robot that will Connect Open Scientists. Research Ideas and Outcomes 2: e9995. https://doi.org/10.3897/rio.2.e9995
Figure 1 - SCINDR, the Science Introduction Robot, watches health research being carried out and, like a good host, introduces people with shared interests, so they can do better work together.
Figure 2 from: Smith C, Todd M, Patiny L, Swain C, Southan C, Williamson A, Clark A (2016) SCINDR - The SCience INtroDuction Robot that will Connect Open Scientists. Research Ideas and Outcomes 2: e9995. https://doi.org/10.3897/rio.2.e9995
Figure 2 - Example of currently functioning Electronic Laboratory Notebook (ELN) entry to which SCINDR will be added. Further enhancements such as real time NMR, LCMS and IR data visualization have been developed and will be added to extend the ELN capability.
Figure 2 from: Ito T, Fukuda T, Morimune T, Hosoya K (2017) Evolution of the connection patterns of the cephalic lateral line canal system and its use to diagnose opsariichthyin cyprinid fishes (Teleostei, Cyprinidae). ZooKeys 718: 115-131. https://doi.org/10.3897/zookeys.718.13574
Figure 2 - Diagram of the cephalic lateral line canal systems in the opsariichthyin fishes. A Candidia barbata, FKUN 34180, 94.8 mm SL B C. pingtungensis, FKUN 35215, 72.9 mm SL C Nipponocypris koreanus, FKUN 40587, 94.1 mm SL. Scale bar 5 mm. D N. sieboldii, FKUN 40571, 90.5 mm SL E N. temminckii, FKUN 40575, 94.5 mm SL F Opsariichthys bidens, LBM 8852, 94.8 mm SL. Scale bar 5 mm. G O. evolans, FKUN 35199, 81.1 mm SL H O. kaopingensis, KUN-P40545, 80.0 mm SL I O. pachycephalus, FKUN 35181, 69.4 mm SL. Scale bar 5 mm. J O. uncirostris, FKUN 16487, 219.0 mm SL K Parazacco spilurus, KUN-P45852, 57.5 mm SL L Zacco platypus, FKUN 40558, 93.0 mm SL. Scale bar 5 mm.
Figure 3 from: Ito T, Fukuda T, Morimune T, Hosoya K (2017) Evolution of the connection patterns of the cephalic lateral line canal system and its use to diagnose opsariichthyin cyprinid fishes (Teleostei, Cyprinidae). ZooKeys 718: 115-131. https://doi.org/10.3897/zookeys.718.13574
Figure 3 - Parsimonious ancestral state reconstruction of the connecting states of the cephalic lateral line canal systems of the opsariichthyin fishes and their out-groups from the maximum likelihood tree inferred from cytochrome b sequences (InL = 12054.39). A The connecting states between the supraorbital (SO) and infraorbital (IO) canals B the connecting states between the temporal (TC) and preoperculomandibular (POM) canals C the connecting states between the left and right sides of the supratemporal canals (ST). The color of each node indicates the connecting states of the cephalic lateral line canal system: black, continuity; white, discontinuity; gray, both sides of the ST connected and extending anteriorly.
Figure 1 from: Ito T, Fukuda T, Morimune T, Hosoya K (2017) Evolution of the connection patterns of the cephalic lateral line canal system and its use to diagnose opsariichthyin cyprinid fishes (Teleostei, Cyprinidae). ZooKeys 718: 115-131. https://doi.org/10.3897/zookeys.718.13574
Figure 1 - Terminology used for cephalic lateral line canal systems: SO supraorbital canal IO infraorbital canal TC temporal canal POM preoperculomandibular canal ST supratemporal canal frb frontal bridge cpb centroparietal bridge itb infratemporal bridge apj anteropteroitic joint.
Development of thalamocortical connectivity during the third trimester
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Supplementary material 2 from: Duquesne E, Fournier D (2024) Connectivity and climate change drive the global distribution of highly invasive termites. NeoBiota 92: 281-314. https://doi.org/10.3897/neobiota.92.115411
Occurrences of the 22 invasive termites as well as their source
Supplementary material 1 from: Duquesne E, Fournier D (2024) Connectivity and climate change drive the global distribution of highly invasive termites. NeoBiota 92: 281-314. https://doi.org/10.3897/neobiota.92.115411
Supplementary tables and figures (S1 to S7)
Refined understanding of hydrological connectivity among lakes on the Qiangtang Plateau
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FlyWire Whole-brain Connectome Connectivity Data
<p>This repository contains the connectivity data for the FlyWire Connectome release. Currently, the latest release is version 783 (see also codex.flywire.ai).</p> <p>The synapses represent a combination of four different data releases, which are combined by the FlyWire whole-brain connectome release. The synapses (as points in space) were detected and published by <a href="https://www.nature.com/articles/s41592-021-01183-7">Buhmann et al., 2021</a> who made use of a cleft segmentation produced by <a href="https://link.springer.com/chapter/10.1007/978-3-030-00934-2_36">Heinrich et al., 2018</a>. The neurotransmitter for these synapses was then predicted and released by <a href="https://www.cell.com/cell/fulltext/S0092-8674(24)00307-6">Eckstein, Bates et al., 2024</a>. The segmentation and neuron IDs (= root IDs) were proofread by the FlyWire consortium and are released by <a href="https://www.nature.com/articles/s41586-024-07558-y">Dorkenwald et al., 2024</a> as part of the <a href="https://www.nature.com/collections/hgcfafejia">FlyWire connectome paper package</a>. </p> <p>Because multiple methods were involved in the production of this resource, the description of the methods is distributed across these manuscripts. We provide a summary in Dorkenwald et al., 2024 (Methods->Synaptic connections).</p> <p>Some of the files made available use feather as a file format. See a code example for reading these files, including a chunk-wise streaming to handle the large file.</p> <h3>flywire_synapses_783.feather</h3> <p>a pandas dataframe with all ~130 million synapses, their locations, neurotransmitter predictions and pre and postsynaptic partners (=root ids). This table contains all synapses that passed the thresholds (see methods section in <a href="https://www.nature.com/articles/s41586-024-07558-y">Dorkenwald et al., 2024</a>), but not all synapses were associated with proofread neurons (e.g., see Discussion->Limitations of our reconstruction in <a href="https://www.nature.com/articles/s41586-024-07558-y">Dorkenwald et al., 2024</a>). Hence, not all root IDs in this table will have a match in the proofread root IDs array. This table is provided for completeness, and to allow calculations about total synaptic input and output of neurons independent of other limitations. </p> <p>Columns:</p> <ul> <li>id: synapse ID</li> <li>pre_pt_root_id: presynaptic neuron ID</li> <li>post_pt_root_id: postsynaptic neuron ID</li> <li>connection_score: score assigned by Buhmann et al.; higher is better. We did not use this score to threshold synapses in any analysis</li> <li>cleft_score: score derived from the cleft segmentation by Heinrich et al.; higher is better. We used a threshold of 50 for all analyses and the released dataset. Synapses with lower score are not made available but can be made available on demand.</li> <li>gaba: probability for neurotransmitter=GABA</li> <li>ach: probability for neurotransmitter=Acetylcholine</li> <li>glut: probability for neurotransmitter=Glutamate</li> <li>oct: probability for neurotransmitter=Octopamine</li> <li>ser: probability for neurotransmitter=Serotonin</li> <li>da: probability for neurotransmitter=Dopamine</li> <li>neuropil: the name of the neuropil associated with this synapse. Symmetric neuropils contain a hemisphere annotation after '_'. E.g., ME_L is the medulla in the left hemisphere. For mapping long names, see Ext. Data Fig. 1 or <a href="https://codex.flywire.ai/app/neuropils">https://codex.flywire.ai/app/neuropils</a></li> <li>post_pt_position_{x,y,z}: Coordinate within the postsynaptic neuron (synapses were identified with two points, one in each neuron). Coordinates are in nanometers.</li> <li>pre_pt_position_{x,y,z}: Coordinate within the presynaptic neuron (synapses were identified with two points, one in each neuron). Coordinates are in nanometers.</li> </ul> <p> </p> <h3>per_neuron_neuropil_count_post_783.feather</h3> <p>a pandas dataframe containing the number of postsynapses per neuropil and segment id, i.e. this is a summarized version of <em>flywire_synapses_783.feather</em></p> <p>Columns:</p> <ul> <li>post_pt_root_id: segment ID</li> <li>neuropil: neuropil name. Symmetric neuropils contain a hemisphere annotation after '_'. E.g., ME_L is the medulla in the left hemisphere. For a mapping to long names see Ext. Data Fig. 1 or <a href="https://codex.flywire.ai/app/neuropils">https://codex.flywire.ai/app/neuropils</a></li> <li>Count: number of synapses for this segment ID and neuropil </li> </ul> <h3> </h3> <h3>per_neuron_neuropil_count_pre_783.feather</h3> <p>a pandas dataframe containing the number of presynapses per neuropil and segment id, i.e. this is a summarized version of <em>flywire_synapses_783.feather</em></p> <p>Columns:</p> <ul> <li>pre_pt_root_id: segment ID</li> <li>neuropil: neuropil name. Symmetric neuropils contain a hemisphere annotation after '_'. E.g., ME_L is the medulla in the left hemisphere. For mapping long names, see Ext. Data Fig. 1 or <a href="https://codex.flywire.ai/app/neuropils">https://codex.flywire.ai/app/neuropils</a></li> <li>Count: number of synapses for this segment ID and neuropil </li> </ul> <h3> </h3> <h3>proofread_root_ids_783.npy</h3> <p>an array of all proofread neuron ids (=root ids)</p> <p> </p> <h3>proofread_connections_783.feather</h3> <p>a pandas dataframe containing the proofread subset from <em>flywire_synapses_783.feather </em>and summarized per neuron-neuron pair and neuropil, i.e. this table contains one entry per neuron-neuron pair and neuropil if there is 1 or more synapses for a given combination</p> <p>Columns:</p> <ul> <li>pre_pt_root_id: presynaptic neuron ID</li> <li>post_pt_root_id: postsynaptic neuron ID</li> <li>neuropil: neuropil name. Symmetric neuropils contain a hemisphere annotation after '_'. E.g., ME_L is the medulla in the left hemisphere. For mapping long names, see Ext. Data Fig. 1 or <a href="https://codex.flywire.ai/app/neuropils">https://codex.flywire.ai/app/neuropils</a></li> <li>syn_count: number of synapses between these two neurons in this neuropil</li> <li>gaba_avg: average probability across the synapses for neurotransmitter=GABA</li> <li>ach_avg: average probability across the synapses for neurotransmitter=Acetylcholine</li> <li>glut_avg: average probability across the synapses for neurotransmitter=Glutamate</li> <li>oct_avg: average probability across the synapses for neurotransmitter=Octopamine</li> <li>ser_avg: average probability across the synapses for neurotransmitter=Serotonin</li> <li>da_avg: average probability across the synapses for neurotransmitter=Dopamine</li> </ul> <p> </p> <h3>Code for reading and streaming feather files</h3> <p>Read feather files with pandas:</p> <p><code>import pandas as pd</code></p> <p><code>df = pd.read_feather(path)</code></p> <p> </p> <p>Stream large feather files in chunks:</p> <p><code>import pyarrow.feather as feather</code></p> <p><code>table = feather.read_table(path)</code></p> <p><code># Total number of rows in the Feather file</code><br><code>num_rows = table.num_rows</code></p> <p><code># Define chunk size</code><br><code>chunk_size = 1000</code></p> <p><code># Read and process the data in chunks</code><br><code>for i in range(0, num_rows, chunk_size):</code><br><code> end_row = min(i + chunk_size, num_rows)</code><br><code> chunk = table.slice(i, end_row - i) # Slice the table from i to end_row</code><br><code> </code></p> <p><code> # Convert to pandas DataFrame if needed</code><br><code> df_chunk = chunk.to_pandas()</code><br><code> </code><br><code> # Now you can process each chunk DataFrame as needed</code><br><code> print(df_chunk.head())</code></p>
UNDERSTANDING DEIXIS: CONNECTING LANGUAGE AND CONTEXT
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Dataset: Connect Biopharma Holdings Limited (CNTB) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
FIGURE 3 in Phylogeography of the banded butterflyfish, Chaetodon striatus, indicates high connectivity between biogeographic provinces and ecosystems in the western Atlantic
FIGURE 3 | Median-joining haplotype network with two datasets: A. mtDNA concatenated (control region and cyt b); B. rag 2. Each color represents one population. Black nodes mean mutational steps.
FIGURE 2 in Phylogeography of the banded butterflyfish, Chaetodon striatus, indicates high connectivity between biogeographic provinces and ecosystems in the western Atlantic
FIGURE 2 | Bayesian skyline plot showing the effective population size fluctuation through time (black line, median estimation; purple lines, confidence interval).
Fig. 5 in Refining the Occurrence of Viral Encephalopathy and Retinopathy, Photobacteriosis, and Vibriosis in Connection with Seawater Physicochemical Parameters: A Five-Year Case Study Abstract
Fig. 5: Estimated time series of the probability of absence/presence (0 to 1) based on the model (depicted by a line), alongside on-site records of absence/presence.
◂Fig. 5 Cells of phylogenetically related strains (light microscopy). a Thecate cell in dorsal view. b Thecate cell in ventral view, note the sulcus extending onto the epitheca (arrow). c Putatively necrotic, thecate cell. d Thecate cell with one bulge on the epitheca (arrow), note that this was the only such cell among thousands of inspected cells. e, f Coccoid cells, apparently without thecae. g Two thecate cells enclosed in the parental theca. h Two connected, immotile cells enclosed in the parental thecae. j Lid of epitheca in dorsal-apical view (mirrored), composed of plates 2′‒4′, all intercalary plates and plates 2′′‒6′′. l‒m Same opened theca in ventral view (l) and dorsal view (m), note the sulcus extending onto the epitheca (arrow), the dorsal opening and all apical and all intercalary plates and plates 3′′‒5′′ remaining with the hypotheca. n Chloroplasts (as inferred from autofluorescence), note the space occupied by the nucleus. Plate labelling follows the Kofoidean notation, n′: apical plate; n′′: precingular plate; n′′′: postcingular plate; na: anterior intercalary plate. Scale= 10 µm in Bumps on the back: An unusual morphology in phylogenetically distinct Peridinium aff. cinctum (= Peridinium tuberosum; Peridiniales, Dinophyceae)
◂Fig. 5 Cells of phylogenetically related strains (light microscopy). a Thecate cell in dorsal view. b Thecate cell in ventral view, note the sulcus extending onto the epitheca (arrow). c Putatively necrotic, thecate cell. d Thecate cell with one bulge on the epitheca (arrow), note that this was the only such cell among thousands of inspected cells. e, f Coccoid cells, apparently without thecae. g Two thecate cells enclosed in the parental theca. h Two connected, immotile cells enclosed in the parental thecae. j Lid of epitheca in dorsal-apical view (mirrored), composed of plates 2′‒4′, all intercalary plates and plates 2′′‒6′′. l‒m Same opened theca in ventral view (l) and dorsal view (m), note the sulcus extending onto the epitheca (arrow), the dorsal opening and all apical and all intercalary plates and plates 3′′‒5′′ remaining with the hypotheca. n Chloroplasts (as inferred from autofluorescence), note the space occupied by the nucleus. Plate labelling follows the Kofoidean notation, n′: apical plate; n′′: precingular plate; n′′′: postcingular plate; na: anterior intercalary plate. Scale= 10 µm
"Main Ideas of 'Data Feminism' and Their Connections from "Data Feminism" by Catherine D'Ignazio and Lauren F. Klein (2023)
<p>The generated graph is a visual representation of the main ideas and principles from the book "Data Feminism" by Catherine D'Ignazio and Lauren F. Klein, supplemented with additional insights from feminist AI ethics. The graph consists of a structured table that outlines key concepts, their descriptions, and their connections to broader themes in data science and AI.</p> <p><strong>Title:</strong> The title at the top of the graph reads "Main Ideas of 'Data Feminism' and Their Connections," emphasizing the comprehensive nature of the ideas presented.</p> <p><strong>Table Content:</strong></p> <ul> <li><strong>Main Idea</strong>: This column lists the core principles of "Data Feminism," such as "Examine Power," "Challenge Power," "Elevate Emotion and Embodiment," "Rethink Binaries and Hierarchies," "Embrace Pluralism," "Consider Context," "Make Labor Visible," "Environmental Impact," and "Consent."</li> <li><strong>Description</strong>: Each main idea is accompanied by a concise description that explains the principle in more detail. For example, "Examine Power" is described as "Analyze power dynamics within data science."</li> <li><strong>Connection</strong>: This column provides context on how each principle connects to broader themes and practices in data science and AI. For instance, "Examine Power" is linked to the idea that "Power influences data collection and interpretation."</li> </ul> <p><strong>Caption:</strong> At the bottom of the graph, a caption provides the sources for the information presented, citing both "Data Feminism" and the authors' subsequent paper "Data Feminism for AI."</p> <p><strong>Visual Design:</strong> The table is neatly arranged with three rows: the title, the main table content, and the caption. The layout is designed to be clear and easy to read, with a focus on conveying the relationships between the principles of "Data Feminism" and their practical applications in AI.</p> <p><strong>Purpose:</strong> The graph serves as a visual summary, making it easier for readers to understand and recall the core ideas of "Data Feminism" and how they can be applied to create more equitable and socially responsible AI technologies.</p> <p>This graph is an addition to the paper, providing a concise and visually appealing overview of the book's main concepts and their relevance to feminist AI ethics.</p> <p>D'ignazio, C., & Klein, L. F. (2023). <em>Data feminism</em>. MIT press.</p> <p>Klein, L., & D'Ignazio, C. (2024, June). Data Feminism for AI. In <em>The 2024 ACM Conference on Fairness, Accountability, and Transparency</em> (pp. 100-112).</p> <p> </p>
ScienceDex guides
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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.