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7,324 results for “pathways”
Data Repository - Distinct Roles of Direct and Indirect Electrification in Pathways to a Renewables-dominated European Energy System
<p>This is the data repository to reproduce the scenario analysis of the paper "<a href="https://www.cell.com/one-earth/fulltext/S2590-3322(24)00037-X?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS259033222400037X%3Fshowall%3Dtrue">Distinct roles of direct and indirect electrification in pathways to a renewables-dominated European energy system</a>".</p> <p>The source code for the REMIND version used in this study is available at <a href="https://github.com/fschreyer/remind/tree/ElecH2_prod">https://github.com/fschreyer/remind/tree/ElecH2_prod</a>. The scenario config file that was used to start the specific model runs of the paper and that inlucdes all scenario-specific model settings can be found in the repository under <a href="https://github.com/fschreyer/remind/blob/ElecH2_prod/config/21_regions_EU11/scenario_config_ElecH2.csv">./config/21_regions_EU11/scenario_config_ElecH2.csv</a>. The repository is a fork with slight changes relative to the main release version available at <a href="https://github.com/remindmodel/remind/tree/v3.2.1">https://github.com/remindmodel/remind/tree/v3.2.1</a> and <a href="https://doi.org/10.5281/zenodo.7852740">https://doi.org/10.5281/zenodo.7852740</a>. The model documentation can be found at <a href="https://rse.pik-potsdam.de/doc/remind/3.2.0">https://rse.pik-potsdam.de/doc/remind/3.2.0</a>. </p> <p>Model output data as well as other data that were used in the study are stored in data.zip. Moreover, we added a PlotsData.zip file, which contains the data shown in the figures of the paper. The R script to produce the figures and analysis of the paper can be found in ElecH2paper_Plots.Rmd. We publish a comprehensive dataset of our model output which includes more data than what is needed to reproduce the figures of the paper. Those data can be helpful to compare and contextualize our scenarios or use them for further analyses. However, due to the scope and complexity of our modeling framework, these data need to be used with care. The data used for the analysis of this study have been thoroughly validated. However, we cannot always perform such validation for the whole dataset and data need to treated with caution in particular at high regional or sectoral resolution and with respect to aspects that were not in the focus of the study as there maybe artefacts or limitations of our modeling approach. Please contact us in case you would like to use our scenarios for further analyses. We welcome open and constructive exchange on our data. </p> <p> </p> <p>Contact:<br>Felix Schreyer<br>Potsdam Institute for Climate Impact Research<br>felix.schreyer@pik-potsdam.de</p>
Dataset for: Oxygen isotope fractionation of O2 consumption through abiotic photochemical singlet oxygen formation pathways
<p>Dataset containing raw and treated isotope-ratio mass spectrometry data as well as O2 concentration data, accompanying the manuscript "Oxygen isotope fractionation of O2 consumption through abiotic photochemical singlet oxygen formation pathways".</p>
Systematic reconstruction of molecular pathway signatures using scalable single-cell perturbation screens
<p>This repo contains Seurat objects, differential expression analysis results, and pathway gene lists for the manuscript "Systematic reconstruction of molecular pathway signatures using scalable single-cell perturbation screens"<br>List of files:</p> <p>1. Seurat_object_IFNB_Perturb_seq.rds: Seurat object of the Perturb-seq data for Interferon-beta pathway<br>2. Seurat_object_IFNG_Perturb_seq.rds: Seurat object of the Perturb-seq data for Interferon-gamma pathway<br>3. Seurat_object_TNFA_Perturb_seq.rds: Seurat object of the Perturb-seq data for TNF-alpha pathway<br>4. Seurat_object_TGFB1_Perturb_seq.rds: Seurat object of the Perturb-seq data for TGF-beta1 pathway<br>5. Seurat_object_INS_Perturb_seq.rds: Seurat object of the Perturb-seq data for insulin pathway<br>6. Pathway_genelist.rds: The pathway gene lists from MultiCCA analysis<br>7. Pathway_Exclusive_genelist.rds: The pathway exclusive gene lists generated from Pathway_genelist.rds<br>8. HClust_Pathway_celltype_specific_genelist.rds: The cell-line specific pathway gene lists from hierarchical clustering analysis independently done on each cell line<br>9. DE_results_all_pathway.zip: The DE test results for all the regulators, cell lines, and pathways (from Mixscale weighted DE test.)<br>10. Bulk_RNAseq_Seurat_object_IFNG_and_TGFB_stim.rds: Seurat object for the bulk RNA-seq data for interferon-gamma and TGF-beta stimulation experiments<br>11. Parse_Guide_Capture_Protocol.pdf: The guide RNA capture protocol developed for Parse Evercode Whole Transcriptome kit</p>
Deep learning models predicting gene functions and pathways using public DRKG knowledge graph and graph neural network
<p>The attached dataset contains pretrained link prediction models, as described in our paper 'Morphological Map of Under- and Over-Expression of Genes in Human Cells'.</p>
Molecular Dynamics simulations suggest possible activation and deactivation pathways in hERG channel
<ol> <li>equil_gating_4_assembly_xleap.prmtop: file topology of the hERG closed state with gating charge 4 equilibration trajectory</li> <li>equil_gating_6_assembly_xleap.prmtop: file topology of the hERG closed state with gating charge 6 equilibration trajectory</li> <li>equil_gating_8_assembly_xleap.prmtop: file topology of the hERG closed state with gating charge 8 equilibration trajectory</li> <li>equil_gating_4.dcd: 100 ns NPT trajectory of the hERG closed state with gating charge 4</li> <li>equil_gating_6.dcd: 100 ns NPT trajectory of the hERG closed state with gating charge 6</li> <li>equil_gating_8.dcd: 100 ns NPT trajectory of the hERG closed state with gating charge 8</li> <li>equil_open_assembly_xleap.prmtop: file topology of the hERG open state equilibration trajectory</li> <li>equil_open.dcd: 100 ns NPT trajectory of the hERG open state</li> <li>herg_closed_gating_4.pdb: PDB file of hERG closed state with gating charge 4 after Steered MD simulations</li> <li>herg_closed_gating_6.pdb: PDB file of hERG closed state with gating charge 6 after Steered MD simulations</li> <li>herg_closed_gating_8.pdb: PDB file of hERG closed state with gating charge 8 after Steered MD simulations</li> <li>TMD_O-C_closed_gating_8_assembly_xleap.prmtop: file topology of the hERG closed state with gating charge 8 TMD trajectory</li> <li>TMD_O-C_closed_gating_6_assembly_xleap.prmtop: file topology of the hERG closed state with gating charge 6 TMD trajectory</li> <li>TMD_O-C_closed_gating_4_assembly_xleap.prmtop: file topology of the hERG closed state with gating charge 4 TMD trajectory</li> <li>TMD_O-C_closed_gating_8.dcd: TMD trajectory of the hERG closed state with gating charge 8</li> <li>TMD_O-C_closed_gating_6.dcd: TMD trajectory of the hERG closed state with gating charge 6</li> <li>TMD_O-C_closed_gating_4.dcd: TMD trajectory of the hERG closed state with gating charge 4</li> </ol> <p>MD trajectories (equilibration and Targeted MD trajectories) in dcd format can be visualized using visualization tools such as VMD or PyMol after uploading the topology file.</p> <p>The directory data_supplementary-note-4.tar.bz2 contains the files related to the Supplementary Notes 4: "A practical example of pathway calculation".</p>
Deciphering the Neurosensory Olfactory Pathway and Associated Neo-Immunometabolic Vulnerabilities Implicated in COVID-Associated Mucormycosis (CAM) and COVID-19 in a Diabetes Backdrop—A Novel Perspective
<p>Raw data files of transcriptomic profiling experiments, which form the basis for our publication (https://www.mdpi.com/2673-4540/3/1/13).</p>
Systematic assessment of pathway databases, based on a diverse collection of user-submitted experiments
<p><strong>Supplemental data for the manuscript </strong></p> <p><strong>"Systematic assessment of pathway databases, based on a diverse collection of user-submitted experiments".</strong></p> <p>Content</p> <ul> <li>functional_annotations.tar.gz <ul> <li> functional annotations for 10 different functional annotation systems, for 5090 species</li> </ul> </li> <li> genome_info_and_statistics.tar.gz <ul> <li>basic genome info, annotation system statistics, user query statistics</li> </ul> </li> <li>example_user_queries.tar.gz <ul> <li>three example files for user query inputs used in the analysis </li> </ul> </li> <li>README.txt <ul> <li>details about the files and file formats</li> </ul> </li> </ul>
Translated Emission Pathways (TEPs): Long-Term Simulations of COVID-19 CO2 Emissions and Thermosteric Sea Level Rise Projections - Supplementary Materials
<p>Supplementary materials for Gonzalez, A. R., & Lin, T. (2022). Translated Emission Pathways (TEPs): Long-Term Simulations of COVID-19 CO<sub>2</sub> Emissions and Thermosteric Sea Level Rise Projections. <em>Earth's Future</em>. In Press.</p> <p><strong>Summary: This study introduces climate science to a broader audience by presenting an accessible research framework and environmental data related to the ongoing COVID-19 pandemic. A series of translated emission pathways (TEPs) were constructed based on the CO<sub>2</sub> emission patterns from the various phases of COVID-19 response. In addition to resembling the forcing scenarios used within climate research, a thermosteric sea level rise analysis was incorporated to further emphasize the environmental benefits that can be obtained from long-term sustainability. As a promising start for including the general public in climate change discussion, this research promotes collective environmental action that mirrors the recommendations of the scientific community.</strong></p>
Amoxicillin degradation pathways and mass spectra raw data (using LC-MS orbitrap)
<p>The link provides five documents namely:</p> <p>File No.1 (Proposed Chemical Structures-tabulated)</p> <p>File No.2 (MS and MS2 images) support for File no.1</p> <p>File No.3 Transformation Products Pathway</p> <p>File No.4 Explanation + Justification of proposed chemical structures</p> <p>Raw Data obtained from compound discoverer</p>
Global high-resolution growth projections dataset for rooftop area consistent with the shared socioeconomic pathways, 2020-2050.
<h2>Description (V2 - Latest):</h2> <p>To enable easy integration in the workflows, we have provided the main datasets in the following formats:</p> <p> </p> <ul> <li><strong><em>Vector dataset:</em><code> Folder - Vector</code> </strong>The global gross estimated rooftop area per FN grid cell for each SSP narrative is provided as a <code><em>Geopackage (.gpkg)</em></code> file <strong>(</strong><strong><em>Results_Vis.gpkg</em></strong><strong>)</strong> with polygon geometries at 1/8-degree spatial resolution in an <strong>EPSG:4326 </strong>coordinate system. The <em>attribute table</em> of this file contains <em>FN_ID</em> column representing the FN grid cell ID, and other columns representing the FN_ID specific assessed rooftop area. The assessed gross rooftop area columns are sequenced as <em>BF_X_Y</em> with <em>X</em> having values as <em>1, 2, 3, 4, and 5</em> for <em>SSP1, SSP2, SSP3, SSP4, SSP5</em><strong><em> </em></strong>narratives with <em>Y</em><strong> </strong>representing the assessment year having values as <em>20, 30, 40, and 50</em> for years <em>2020, 2030, 2040, and 2050</em> and with <strong><em>km<sup>2</sup></em></strong> units. In addition, a CF column is added for each FN_ID entry that documents the Capacity Factor for rooftop solar PV based on the World Bank solar atlas.</li> </ul> <p> </p> <ul> <li><strong><em>Raster datasets:</em></strong><strong> <code> Folder - Raster</code> </strong>The global gross estimated rooftop area per FN grid cell for each SSP narrative is provided as a <code><em>geotiff (.tif)</em></code> files with <strong>LZW</strong> compression in an <strong>EPSG:4326</strong> coordinate system. The assessed gross rooftop area datasets are sequenced as <em>BF_X_Y</em> with <em>X</em> having values as <em>1, 2, 3, 4, and 5 </em>for <em>SSP1, SSP2, SSP3, SSP4, SSP5</em><strong><em> </em></strong>narratives with<strong> </strong><em>Y</em> representing the assessment year having values as <em>20, 30, 40, and 50</em> for years <em>2020, 2030, 2040, and 2050</em><strong> </strong>and with <strong><em>km<sup>2</sup></em></strong> units.</li> </ul> <p> </p> <ul> <li><strong><em>Numerical dataset:</em></strong> <strong><code> Folder - Numerical</code> </strong>The global gross estimated rooftop area per FN grid cell for each SSP narrative is provided as a <code><em>parquet (.parquet)</em></code> file <strong><em>(Results.parquet).</em></strong> This file contains <em>FN_ID</em> column representing the FN grid cell ID, and other columns representing the FN_ID specific assessed rooftop area. The assessed gross rooftop area columns are sequenced as <em>BF_X_Y</em> with <em>X</em> having values as <em>1, 2, 3, 4, and 5</em> for <em>SSP1, SSP2, SSP3, SSP4, SSP5</em> narratives with <em>Y </em>representing the assessment year having values as<strong> </strong><em>20, 30, 40, and 50</em><strong> </strong>for years <em>2020, 2030, 2040, and 2050</em><strong> </strong>and with <strong><em>km<sup>2</sup></em></strong> units. In addition, a <em>CF</em> column is added for each FN_ID entry that documents the Capacity Factor for rooftop solar PV based on the World Bank solar atlas.</li> </ul> <p> </p> <p>In addition to the main datasets, we have provided additional files to enable generating the vector and numerical datasets from this study: <strong><code> Folder - Models</code></strong></p> <ul> <li><strong><em>M2_Model.json:</em></strong><strong> </strong>This file contains the frozen parameters of the M2 model in <code><em>.json</em></code> format generated from <code>XGBoost version 2.0.3</code></li> <li><strong><em>SSP_drivers.parquet:</em><em> </em></strong>This file contains the driver data used for generating the main dataset in our study</li> <li><strong><em>FN_MAP.parquet:</em></strong><strong> </strong>This file contains the boundary information for each fishnet grid tile in a Well Known Text <em>(WKT)</em> format.</li> <li><strong><em>Prediction.ipynb:</em></strong><strong> </strong>This file provides a python notebook interface to generate inferencing from <em><code>M2_Model.json</code> </em>using <code><em>SSP_drivers.parquet</em></code> file. In addition, this file also generates the numerical dataset and converts it into vector dataset using <code><em>FN_MAP.parquet</em></code><code> </code>file.</li> <li><strong><em>environment.yaml:</em></strong><strong> </strong>This file contains the frozen configuration of python virtual environment used to generate the results presented in this study.</li> </ul> <p> </p> <h2><strong>Version history:</strong></h2> <p><strong>This version corresponds to the revised journal submission (Round 1). <em>The version will be updated upon the completion of the review of the main manuscript.</em></strong></p> <ul> <li><em>This version <strong>V2</strong> is supersedes <strong>V1</strong> to correspond with round 1 of review.</em></li> <li>The database(s) in this version is associated with a Data Descriptor paper manuscript entitled " <em>Global high-resolution growth projections for rooftop area consistent with the shared socioeconomic pathways, 2020-2050 </em>", submitted to <em>Scientific Reports</em> Journal (<a href="https://www.nature.com/srep/">https://www.nature.com/srep/</a>)</li> </ul> <p> </p> <h2>Changelog:</h2> <p>The following files from version <strong>V1</strong> of this dataset are now <strong><em>archived</em></strong> based on the reviews (Round 1).</p> <ol> <li> <blockquote><em><strong>1_Geospatial_Dataset_V1.gpkg</strong></em></blockquote> </li> <li> <blockquote><em><strong>2_Countrylevel_gross_rooftop_area_V1.parquet</strong></em></blockquote> </li> <li> <blockquote><em><strong>3_Analytics_Scripts_V1.ipynb</strong></em></blockquote> </li> </ol>
RNA-seq dataset for Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism
<p>Data to be used along with <a href="https://github.com/neelroop/asd-development-coexpression-2013">code</a> from 2013 paper that was originally on a site hosted at UCLA, but may no longer be accessible.</p>
Interstage single ventricle heart disease infants show dysregulation in multiple metabolic pathways: targeted metabolomics analysis - Data
<p>The data in this Zenodo entry corresponds to the data used to produce the results in <a href="https://www.jacc.org/doi/full/10.1016/j.jacadv.2022.100169">https://www.jacc.org/doi/full/10.1016/j.jacadv.2022.100169</a>. The zipped folder contains three files</p> <ul> <li>Metabolite Data.csv - The meatobilte measurements for all the samples</li> <li>Clinical Data.csv - Values for the clinical variables</li> <li>Clinical Data Descriptions.csv - More in depth explanation of clinical variables as well as possible values of the variables</li> </ul> <p><span>This study was supported by the American Heart Association (AHA</span><span>20CDA35310498 and AHA18IPA34170070) and the National Institutes </span><span>of Health (NIH/NCATS Colorado CTSA, No. UL1 TR001082 and NIH/</span><span>NHLBI K23HL12363</span></p>
"The pathway of hyaluronic acid (HA) and its receptors (CD44, RHAMM) in the regulation of Rho GTPases and their effectors in an in vitro colorectal cancer model" ("Szlak kwasu hialuronowego (HA) i jego receptorów (CD44, RHAMM) w regulacji GTPaz Rho i ich efektorów w modelu raka jelita grubego in vitro"); NCN Miniatura 2022/06/X/NZ3/00848
<p>Results from Screening for "The pathway of hyaluronic acid (HA) and its receptors (CD44, RHAMM) in the regulation of Rho GTPases and their effectors in an in vitro colorectal cancer model" the project <strong>Miniatura</strong> (<strong>2022/06/X/NZ3/00848</strong>) funded by Polish <strong>National Science Centre (NCN)</strong></p> <p>Wyniki skriningu w projekcie "Szlak kwasu hialuronowego (HA) i jego receptorów (CD44, RHAMM) w regulacji GTPaz Rho i ich efektorów w modelu raka jelita grubego in vitro", <strong>Miniatura</strong> (<strong>2022/06/X/NZ3/00848</strong>) finansowanym przez <strong>Narodowe Centrum Nauki (NCN)</strong></p>
Data for Identifying Robust Decarbonization Pathways for the Western U.S. Electric Power System under Deep Climate Uncertainty
<p>Data from the paper "Identifying Robust Decarbonization Pathways for the Western U.S. Electric Power System under Deep Climate Uncertainty"</p>
Promiscuity cliffs (PCs), promiscuity cliff pathways (PCPs), and promiscuity hubs (PHs) formed by inhibitors of human kinases
<p>The PC, PCP, and PH data structures have been introduced for the analysis of compound promiscuity [1-3]. A comprehensive collection of PCs, PCPs, and PHs formed by kinase inhibitors covering more than 80% of the human kinome is made available. See readme.txt for more information regarding the provided files.</p> <p>References:</p> <ol> <li>Dimova, D.; Gilberg, E.; Bajorath, J. Identification and Analysis of Promiscuity Cliffs Formed by Bioactive Compounds and Experimental Implications. RSC Adv. 2017, 7, 58–66.</li> <li>Miljković, F.; Bajorath, J. Computational Analysis of Kinase Inhibitors Identifies Promiscuity Cliffs across the Human Kinome. ACS Omega 2018, 3, 17295–17308.</li> <li>Miljković, F; Vogt, M; Bajorath, J. Systematic Computational Identification of Promiscuity Cliff Pathways Formed by Inhibitors of the Human Kinome. J. Comput. Aided Mol. Des. 2019, in press, doi: doi.org/10.1007/s10822-019-00198-9</li> </ol>
Pathway Figure OCR GMT
<p>Sample Pathway Figure OCR results from 17 December 2018, condensed by PMCID into GMT format for use in gene set analysis. Columns are PMCID, URL, Entrez Gene IDs</p>
Guest-Mediated Modulation of Photophysical Pathways in a Coronene Bisimide Cyclophane
<p>Additional data to report <a href="https://doi.org/10.1021/jacs.4c08479">https://doi.org/10.1021/jacs.4c08479</a>: </p> <p>The properties and functions of chromophores utilized by nature are strongly affected by the environment formed by the protein structure in the cells surrounding them. This concept is transferred here to host−guest complexes with the encapsulated guests acting as an environmental stimulus. A new cyclophane host based on coronene bisimide is presented that can encapsulate a wide variety of planar guest molecules with binding constants up to (4.29 ± 0.32) × 10<sup>10</sup> M<sup>−1</sup> in chloroform. Depending on the properties of the chosen guest, the excited state deactivation of the coronene bisimide chromophore can be tuned by the formation of host−guest complexes toward fluorescence, exciplex formation, charge separation, room-temperature phosphorescence (RTP), or thermally activated delayed fluorescence (TADF). The photophysical processes were investigated by absorption, emission, and femto- and nanosecond transient absorption spectroscopy. To enhance the TADF, two different strategies were used by employing suitable guests: the reduction of the singlet−triplet gap by exciplex formation and the external heavy atom effect. Altogether, by using supramolecular host−guest complexation, a versatile multimodal chromophore system is achieved with the coronene bisimide cyclophane.</p>
Excel data collection template on descriptive political representation in national parliaments of the projects Pathways to Power and InclusiveParl adapted for the ActEU project
<p>This file contains the empty data collection template and variable and value labels to code biographical data on legislators for WP4 in the ActEU project. It is an abbreviated version of the codebooks produced by the Pathways to Power project and by the InclusiveParl project.</p>
GCAM input files for "Decarbonization pathways for Korea's industrial sector towards its 2050 carbon neutrality goal"
<p>GCAM input files for "Decarbonization pathways for Korea's industrial sector towards its 2050 carbon neutrality goal"</p>
Data set accompanying the research article "Complete representation of action space and value in all striatal pathways"
<p>GCaMP6s calcium imaging data set recorded from freely behaving mice performing open field and 2-choice decision-making tasks using miniscopes. Mice were implanted in the right dorsomedial striatum and three types of output neurons were genetically targeted using transgenic Cre-lines. The data set comprises single-cell spatial filters and calcium activity traces extracted using CaImAn (https://github.com/flatironinstitute/CaImAn) as well as behavioral event logs and tracking coordinates. For more details please refer to the article "Complete representation of action space and value in all striatal pathways" published by the data sets' authors. Analysis code can be found at https://doi.org/10.5281/zenodo.5034618.</p>
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.