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7,327 results for “pathways”
Data from: Parallel molecular evolution in pathways, genes, and sites in high-elevation hummingbirds revealed by comparative transcriptomics
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Food matters: Dietary shifts increase the feasibility of 1.5°C pathways in line with the Paris Agreement
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Genomics of new ciliate lineages provides insight into the evolution of obligate anaerobiosis - single gene datasets for phylogenomic analysis of anaerobic ciliates (SAL, Ciliophora), protein datasets for mitochondrial pathways prediction, and mitochondrial genomes
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Data from: Rotavirus NSP1 subverts the antiviral oligoadenylate synthetase-RNase L pathway by inducing RNase L degradation
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Data from: Reproductive resilience: Pathways to gametogenic success in Montipora capitata after bleaching
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Data from: Neural pathways of autonomic reflexes in familial autonomic ganglionopathy
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Targeted metabolomics data for dimethyl-ribityl lumazine (DMRL) and riboflavin in Mycobacterium tuberculosis and Mycolicibacterium smegmatis mutants of the riboflavin biosynthetic pathway
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Data from: Solanum lycopersicum CLASS-II KNOX genes regulate fruit anatomy via gibberellin-dependent and independent pathways
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Data from: Biogeochemical patterns in prey species reveal complex mercury exposure pathways from the environment to Aleutian Steller sea lions
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Ventral pallidum efferent pathways via mediodorsal thalamus and lateral habenula mediate distinct aspects of default mode network regulation
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Task-induced modulations of neuronal activity along the auditory pathway
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The evolution of multi-gene families and metabolic pathways in the evening primroses (Oenothera: Onagraceae): a comparative transcriptomics approach
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Pathway enrichments from untargeted metabolomics of plasma of Peromyscus leucopus and Mus musculus with or without LPS treatment
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Dateset The "blue eyed" in our school – overcoming stereotypes as the pathway to creating better conditions for pupil development in the school class - control group and equal socioeconomic status groups
<p>Dateset The “blue eyed” in our school – overcoming stereotypes as the pathway to creating better conditions for pupil development in the school class - control group and equal socioeconomic status groups</p>
Reactome HSA v71 pathways and hierarchy
<p>Originally from:<strong> https://reactome.org/download-data</strong><br> Version 71: <strong>https://reactome.org/about/news/145-version-71-releases</strong></p> <p>What was done here:<br> Top pathways found in <strong>https://reactome.org/PathwayBrowser/</strong> were manually added as children to the parent R-HSA-0000000 in the file "NewestReactomeNodeRelations.txt". These top pathway descriptions were also added to the file "Top_Human_REACTOME_Nodes_ManualCuration.txt"</p> <p>The rest of the pathway parent child relationships were taken from:<br> <strong>https://reactome.org/download/current/ReactomePathwaysRelation.txt</strong><br> but only the R-HSA pathways were used and added to the file "NewestReactomeNodeRelations.txt"</p> <p>The pathway definitions are the ones found in<br> "Ensembl2Reactome_All_Levels_v71.txt"<br> </p>
Open Science and Career Pathways
<p><strong>Episode Summary:</strong></p> <p>In this episode we are discussing researcher career options and how open science can have career benefits. Our interview guest will be <a href="https://www.vitae.ac.uk/events/event-presenters/dr-janet-metcalfe">Dr Janet Metcalfe</a> who is the Head of Vitae, an organisation supporting researcher professional development. We will cover the challenges researchers face in modern academia, the surprising truth about changing career paths, and how Open Science can benefit research career progression. </p> <p><strong>Resources and Links:</strong></p> <ul> <li><a href="https://www.vitae.ac.uk/%20">Vitae</a></li> <li><a href="https://qz.com/547641/theres-an-awful-cost-to-getting-a-phd-that-no-one-talks-about/">Cost of Getting a PhD (Article)</a></li> </ul> <p><strong>Episode Quotes:</strong></p> <p>“People, when they are doing their PhD, they tend to measure their PhD in terms of the research results at the end of it, but actually the most important product of a PhD is the person.”</p>
Supplementary code and visualisations for "Seizure pathways change on circadian and slower timescales in individual patients with focal epilepsy"
<p>MATLAB code and workspaces for reproducing the main results and figures of the paper "Seizure pathways change on circadian and slower timescales in individual patients with focal epilepsy." Additional supplementary visualisations for all patients are also provided as PDFs. </p> <p>The paper preprint is available on bioRxiv: https://www.biorxiv.org/content/10.1101/661371v2</p>
Leveraging Heterogeneous Network Embedding for Metabolic Pathway Prediction
<p>We include samples of various data types used in the work "Leveraging Heterogeneous Network Embedding for Metabolic Pathway Prediction"</p> <p>More information about the software package and instructions are provided in <a href="https://github.com/hallamlab/pathway2vec">hallamlab/pathway2vec</a></p>
Data Supplement: U.S. state-level projections of the spatial distribution of population consistent with Shared Socioeconomic Pathways.
<p>These data are to supplement the following in-press publication: </p> <p>Zoraghein, H., and O'Neill B. (2020). U.S. state-level projections of the spatial distribution of population consistent with Shared Socioeconomic Pathways. Sustainability.</p> <p>The data herein were generated using the `population_gravity` model which can be found here: <a href="https://github.com/IMMM-SFA/population_gravity">https://github.com/IMMM-SFA/population_gravity</a></p> <p>CONTENTS:</p> <p><strong>zoraghein-oneill_population_gravity_inputs_outputs.zip</strong></p> <ul> <li>contains a directory for each U.S. state for inputs and outputs</li> <li><strong>inputs</strong> contain the following: <ul> <li><strong><state-name>_<urban or rural>_<yr>_1km.tif: </strong> Urban and Rural population GeoTIF rasters at a 1km resolution <ul> <li>value per grid cell: number of humans (float)</li> <li>crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected</li> <li>nodata value: -3.40282e+38</li> </ul> </li> <li><strong><state-name>_mask_short_term.tif: </strong> Mask GeoTIF rasters at a 1km resolution that contain values from 0.0 to 1.0 for each 1 km grid cell to help calculate suitability depending on topographic and land use and land cover characteristics <ul> <li>value per grid cell: values from 0.0 to 1.0 (float) that are generated from topographic and land use and land cover characteristics to inform suitability as outlined in the companion publication</li> <li>crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected</li> <li>nodata value: -3.40282e+38</li> </ul> </li> <li><strong><state-name>_<ssp>_popproj.csv: </strong> Population projection CSV files for urban, rural, and total population (number of humans; float) for SSPs 2, 3, and 5 for years 2010-2100</li> <li><strong><state-name>_coordinates.csv: </strong>CSV file containing the coordinates for each 1 km grid cell within the target state. File includes a header with the fields XCoord, YCoord, FID.,Where data types and field descriptions are as follows: (XCoord, float, X coordinate in meters),(YCoord, float, Y coordinate in meters),(FID, int, Unique feature id)</li> <li><strong><state-name>_within_indices.txt: </strong>text file containing a file structured as a Python list (e.g. [0, 1]) that contains the index of each grid cell when flattened from a 2D array to a 1D array for the target state.</li> <li><strong><state-name>_<ssp>_params.csv: </strong>CSV file containing the calibration parameters (alpha_rural, beta_rural, alpha_urban, beta_urban; float) for the `population_gravity` model for each year from 2010-2100 in 10-year time-steps as described in the companion publication</li> </ul> </li> <li><strong>outputs</strong> contain the following: <ul> <li><strong>jones_oneill</strong> directory; these are the comparison datasets used to build Figures 7 and 8 in the companion publication <ul> <li>contains three directories: SSP2, SSP3, and SSP5 that each contain a GeoTIF representing total population (number of humans; float) at 1km resolution for years 2050 and 2100. <ul> <li><strong><state-name>_1km_<ssp>_total_<year>_jones_oneill.tif:</strong> <ul> <li>value per grid cell: number of humans (float)</li> <li>crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected</li> <li>nodata value: -3.40282e+38</li> </ul> </li> </ul> </li> </ul> </li> <li><strong>model</strong> directory; these are the model outputs from `population_gravity` for SSP2, SSP3, and SSP5 that each contain a GeoTIF representing urban, rural, and total population (number of humans; float) at 1km resolution for years 2020-2100 in 10-year time-steps. <ul> <li><strong><state-name>_1km_<ssp>_<urban, rural, or total>_<year>_jones_oneill.tif:</strong> <ul> <li>value per grid cell: number of humans (float)</li> <li>crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected</li> <li>nodata value: -3.40282e+38</li> </ul> </li> </ul> </li> </ul> </li> </ul> <p> </p> <p><strong>zoraghein-oneill_population_gravity_national-ssp-maps.zip</strong></p> <ul> <li>Results of the `population_gravity` model mosaicked to the National scale at a 1km resolution and the comparison Jones and O'Neill research. These are used to generate Figure 6 of the companion paper <ul> <li><strong>National_1km_<ssp>_<urban, rural, or total>_<year>_jones_oneill.tif:</strong> <ul> <li>value per grid cell: number of humans (float)</li> <li>crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected</li> <li>nodata value: -3.40282e+38</li> </ul> </li> <li><strong>National_1km_<ssp>_<urban, rural, or total>_<year>.tif:</strong> <ul> <li>value per grid cell: number of humans (float)</li> <li>crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected</li> <li>nodata value: -3.40282e+38</li> </ul> </li> </ul> </li> </ul> <p> </p>
Metabolic pathway inference using multi-label classification with rich pathway features
<p>We include samples of various data types used in the work "Metabolic pathway inference using multi-label classification with rich pathway features"</p> <p>More information about the software package and instructions are provided in <a href="https://github.com/hallamlab/mlLGPR">hallamlab/mlLGPR</a></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.