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5,145 results for “CO₂”
FIGURE 5 in First Velarifictorus (Orthoptera: Gryllidae, Gryllinae) cricket described from Borneo (Southeast Asia) and notes on a co-occurring congener
FIGURE 5. Receiving Operating Characteristic (ROC) curve for Uelarifictorus aspersus aspersus (Walker, 1869) in Southeast Asia excluding (A) and including (B) the presence data in Brunei Darussalam.
FIGURE 4 in First Velarifictorus (Orthoptera: Gryllidae, Gryllinae) cricket described from Borneo (Southeast Asia) and notes on a co-occurring congener
FIGURE 4. Map of Southeast Asia with red dots indicating the localities of Uelarifictorus aspersus aspersus (Walker, 1869) used for both bias grid and for MaxEnt modelling.
FIGURE 3 in First Velarifictorus (Orthoptera: Gryllidae, Gryllinae) cricket described from Borneo (Southeast Asia) and notes on a co-occurring congener
FIGURE 3. Uelarifictorus aspersus aspersus (Walker, 1869) male calling song from Brunei: Oscillograms (A, B), frequency spectrogram (C) and amplitude-frequency spectrogram (D).
FIGURE 1 in First Velarifictorus (Orthoptera: Gryllidae, Gryllinae) cricket described from Borneo (Southeast Asia) and notes on a co-occurring congener
FIGURE 1. Uelarifictorus temburongensis sp. nov. male: head in frontal view (A), head and pronotum in dorsal view (B), anterior half of habitus in profile (C), tegmen in dorsal view (D), male phallic complex in dorsal (E, F), ventral (G) and lateral (H) views. Scale bars: 1 mm (A), 2 mm (B–D).
FIGURE 7 in First Velarifictorus (Orthoptera: Gryllidae, Gryllinae) cricket described from Borneo (Southeast Asia) and notes on a co-occurring congener
FIGURE 7. Predicted distribution of Uelarifictorus aspersus aspersus (Walker, 1869) in Southeast Asia excluding (A) and including (B) the presence data in Brunei Darussalam.
FIGURE 6 in First Velarifictorus (Orthoptera: Gryllidae, Gryllinae) cricket described from Borneo (Southeast Asia) and notes on a co-occurring congener
FIGURE 6. Jackknife test for Uelarifictorus aspersus aspersus (Walker, 1869) in Southeast Asia excluding (A) and including (B) the presence data in Brunei Darussalam.
Supplementary data and microkinetic model for 'Key Role of CO Coverage for Chain Growth in Co-Based Fischer-Tropsch Synthesis'
<p>This repository contains:</p> <ol> <li>The DFT data (energies, frequencies and structures) of all important intermediates of the microkinetic model constructed for the publication ‘Key Role of CO Coverage for Chain Growth in Co-Based Fischer-Tropsch Synthesis’.</li> <li>Input files for the high CO coverage microkinetic model in Chemkin.</li> <li>(update 2025-06-19) Input files for the high CO coverage microkinetic model in Chemkin with CO2 activation (sim2.zip).</li> </ol> <p>DOI: <u>10.1021/acscatal.5c03024</u> and <u>10.1021/acscatal.3c04844</u></p>
Global Carbon Monoxide (CO) Flux Estimates for 2001-2015
<p>This data set contains Global carbon monoxide (CO) flux estimates for 2001-2015 partitioned into biomass burning (BB), fossil fuel (FF) and biogenic (BG) sources. The estimates were created at JPL/Caltech by Anthony Bloom using a Metropolis-Hastings Markov Chain Monte Carlo (MCMC) algorithm (Bloom et al., 2015) applied to top-down CO fluxes obtained from inverse modeling using the GEOS-Chem (with adjoint) model and data from the Terra/MOPITT satellite (Jiang et al., 2017). The spatial resolution is 4.0 x 5.0 degrees lat/lon.</p> <p>Examples of the use of this data are described in Worden, J., et al., 2017 and Worden, H. et al., 2019.</p> <p>References:</p> <p>Bloom, A. A., J. Worden, Z. Jiang, H. Worden, T. Kurosu, C. Frankenberg, D. Schimel, (2015), Remote sensing constraints on South America fire traits by Bayesian fusion of atmospheric and surface data, Geophysical Research Letters, doi:10.1002/2014GL062584</p> <p>Jiang, Z., J. R. Worden, H. Worden, M. Deeter, D. B. A. Jones, A. F. Arellano, and D. K. Henze (2017), A 15-year record of CO emissions constrained by MOPITT CO observations, Atmos. Chem. Phys., 17(7), 4565–4583, doi:10.5194/acp-17-4565-2017.</p> <p>Worden, J. R., A.A. Bloom, S. Pandey, Z. Jiang, H.M. Worden, T.W. Walker, S. Houweling, T. Röckmann, (2017), Reduced biomass burning emissions reconcile conflicting estimates of the post-2006 atmospheric methane budget, Nature Communications, 8:2227, doi:10.1038/s41467-017-02246-0.</p> <p>Worden, H. M., Bloom, A. A., Worden, J. R., Jiang, Z., Marais, E., Stavrakou, T., Gaubert, B., and Lacey, F.: New Constraints on Biogenic Emissions using Satellite-Based Estimates of Carbon Monoxide Fluxes, Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2019-377, in review, 2019.</p>
MOPITT CO monthly climatology 2002-2021
<p>Monthly CO daytime measurements from the satellite-based instrument MOPITT, averaged between 2002 and 2021. We use level 3, version 9 joint NIR/TIR retrievals (doi: 10.5067/TERRA/MOPITT/MOP03JM.009). These data have been compiled for the NCAR ADF model evaluation package (https://github.com/NCAR/ADF).</p> <p>Note: August and September averages are missing 2009 data due to instrument downtime.</p>
Does Co-Development with AI Assistants Lead to More Maintainable Code? Replication Package
<p>This is the replication package for the study "Echoes of AI: Investigating the Downstream Effects of AI Assistants on Software Maintainability" preregistered aat ICSME 2024 as “Does Co-Development with AI Assistants Lead to More Maintainable Code?”</p> <p>Abstract from the registered report:</p> <p>[Background/Context] AI assistants like GitHub Copilot are transforming software engineering, with several studies highlighting productivity improvements. However, their impact on code quality, particularly in terms of maintainability, requires further investigation.<br>[Objective/Aim] This study aims to examine the influence of AI assistants on software maintainability, specifically assessing how these tools affect the ability of developers to evolve code.<br>[Method] We will conduct a two-phased controlled experiment involving professional developers. In Phase 1, developers will add a new feature to a Java project, with or without the aid of an AI assistant. Phase 2, a randomized controlled trial, will involve a different set of developers evolving random Phase 1 projects - working without AI assistants. We will employ Bayesian analysis to evaluate differences in completion time, perceived productivity, code quality, and test coverage.</p> <p>Note: To maintain the integrity of the study, i.e., preventing any leakage to AI assistants' training data, we choose not to host the code in a public git repository. Instead, all relevant documents and code are shared through a replication package on Zenodo, available as PDF documents generated by repo2pdf (https://github.com/BankkRoll/repo2pdf). We have deliberately used settings to obfuscate the code (e.g., line numbers) to ensure it will not be scraped by any large language models before the study has been completed.</p> <p>Contents:</p> <ul> <li>Task 1 instructions.</li> <li>Task 2 instructions.</li> <li>The source code that the participants received.</li> <li>A causal graph with analysis details.</li> <li>Archives containing anonymized experimental data and analysis scripts (in .zip and .tar.gz for convenience). </li> </ul>
Confocal image stack of aPKC/FoxP co-staining
<p>Confocal image stacks of whole mount preparations of central nervous systems of adult Drosophila.</p><p>Genotype: aPKC-Gal4>CD8::GFP, red - FoxP-LexA>CD8::RFP; D: green - D42-Gal4>CD8::GFP, red - FoxP-LexA>CD8::RFP. Confocal image stacks available at: </p>
"On the Prevalence, Co-occurrence, and Impact of Infrastructure-as-Code Smells" Replication Package
<p>In this package, we provide the dataset for the paper: " On the Prevalence, Co-occurrence, and Impact of Infrastructure-as-Code Smells ''</p><p> </p><p>1 – we provide the generated data for each of the research questions.</p><p> </p><p>2 – we provide the scripts for each of the research questions.</p>
Figure 3 in Repeated evolution of sympatric, palaeoendemic species in closely related, co-distributed lineages of Hemiphyllodactylus Bleeker, 1860 (Squamata: Gekkonidae) across a sky-island archipelago in Peninsular Malaysia
Figure 3. Habitat at the type locality of Hemiphyllodactylus bintik sp. nov., Gunung Tebu, Terenganu, Peninsular Malaysia.
Figure 4 in Repeated evolution of sympatric, palaeoendemic species in closely related, co-distributed lineages of Hemiphyllodactylus Bleeker, 1860 (Squamata: Gekkonidae) across a sky-island archipelago in Peninsular Malaysia
Figure 4. The results of the different partitioning schemes on node age estimates. When applicable, node age estimates from Heinicke et al. (2011) for nuclear DNA (nDNA) only and combined mitochondrial (mtDNA) and nDNA were includ- ed for comparative purposes.
Figure 2 in Repeated evolution of sympatric, palaeoendemic species in closely related, co-distributed lineages of Hemiphyllodactylus Bleeker, 1860 (Squamata: Gekkonidae) across a sky-island archipelago in Peninsular Malaysia
Figure 2. Dorsal and ventral view of the holotype of Hemiphyllodactylus bintik sp. nov. (LSUHC 11216).
Figure 1. A, Bayesian time tree for Hemiphyllodactylus with 95 in Repeated evolution of sympatric, palaeoendemic species in closely related, co-distributed lineages of Hemiphyllodactylus Bleeker, 1860 (Squamata: Gekkonidae) across a sky-island archipelago in Peninsular Malaysia
Figure 1. A, Bayesian time tree for Hemiphyllodactylus with 95% highest posterior density (95% HPD) intervals for major nodes represented by purple bars. Black circles at nodes are posterior probabilities ≥ 0.95; grey circles at nodes are posterior probabilities <0.95. B, Bayesian time tree for the Hemiphyllodactylus harterti group. C, Distribution of the H. harterti group in Peninsular Malaysia.
Pre- and co-seismic landslides of the Sept. 5 Luding earthquake
<p>This folder contains the pre- and co-seismic landslides inventories, intensity circle, and seismogenic fault of the Mw 6.6 Luding, China earthquake. The earthquake occurred on 2022-09-05 at 04:52 UTC, the hypocenter was located at 29.61° N, 102.03° E at a depth of 13.0 km. The mapping region encompasses the intensity IX circle of the earthquake.</p>
Valorization of plastic wastes into value-add biochar production through co-pyrolysis with biomass residues
Open the record for dataset details and reuse information.
Stage-specific expression patterns and co-targeting relationships among miRNAs in the developing mouse cortex
<p>Analysis of expression patterns and co-targeting relationships between miRNAs in the embryonic mouse cortex. The following supplementary data are uploaded:</p><p>Supplementary_table_1.xlsx: Differentially expressed miRNAs between E14, E17 and P0 cortical samples as well as in NPCs isolated from the mouse cortex and differentiated into neurons in vitro.</p><p>Supplementary_table_2.xlsx: Weighted co-expression gene network analysis of miRNAs in the embryonic mouse cortex.</p><p>Supplementary_table_3.xlsx: Gene ontology terms of miRNA targets of the black and green modules from the WCGNA analysis.</p><p>Supplementary_table_4.xlsx: Significant co-targeting relationships between miRNAs in the embryonic mouse cortex.</p><p> </p><p> </p>
Data and code for Fitzgerald et al: MDD seeded co-expression networks
<p>Below is a decription of the data and code supplied within this repository related to Fitzgerald et al "Astrocyte fatty acid metabolism as a driver of risk for major depressive disorder"</p> <table> <tbody> <tr> <td>Generated data </td> </tr> <tr> <td>Data</td> <td>About</td> <td> </td> </tr> <tr> <td>All_GTEx_DLPFC_networks.RData</td> <td>Non-thresholded coexpression summary statistics for MDD risk genes in GTEx frontal cortex</td> <td> </td> </tr> <tr> <td>my_big_negative_GTEx_DLPFC_list.RData</td> <td>"All_GTEx_DLPFC_networks.Rdata" data filtered to those genes with R < -0.5 and FDR < 0.05</td> <td> </td> </tr> <tr> <td>my_big_positive_GTEx_DLPFC_list.RData</td> <td>"All_GTEx_DLPFC_networks.Rdata" data filtered to those genes with R > 0.5 and FDR < 0.05</td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>Generated code</td> </tr> <tr> <td>File</td> <td>About</td> <td>Related figure</td> </tr> <tr> <td>Chang_bootstrap.R</td> <td>Bootstrapping of coexpression networks in the Chang et all data for comparing FADS1 coexpressed genes across disease states</td> <td>Fig 4G</td> </tr> <tr> <td>CMC_QC.R</td> <td>Quality control for the common mind consortium data for validation of coexpression networks</td> <td>Supp</td> </tr> <tr> <td>Cont_vs_MDD_modscores.R</td> <td>Generating module scores in snRNA-seq data</td> <td>Fig 4E</td> </tr> <tr> <td>FADS1_clustering.R</td> <td>Clustering of snRNA-seq data using genes coexpressed with FADS1</td> <td>Fig 5</td> </tr> <tr> <td>gene_analysis.sh</td> <td>Annotation of GWAS summary statistics using Hi-C data</td> <td>Fig 2A</td> </tr> <tr> <td>gene_set_analysis.sh</td> <td>GWAS enrichment analysis using MAGMA</td> <td>Fig 6C</td> </tr> <tr> <td>GTEx_coexp_networks.R</td> <td>Generating seeded coexpression networks for MDD risk genes in the GTEx dataset</td> <td>Fig 2B</td> </tr> <tr> <td>GTEx_QC_1.R</td> <td>Filtering of the GTEx dataset</td> <td>NA</td> </tr> <tr> <td>GTEx_QC_2.R</td> <td>Normalisation and regression of technical covariates from the GTEx data</td> <td>NA</td> </tr> <tr> <td>Labonte_et_al_QC.R</td> <td>Quality control, filtering and regression of technical covariates from the Labonte et al dataset</td> <td>Fig 4F</td> </tr> <tr> <td>Milo_analysis.R</td> <td>Neighbourhood based analysis for differentially abundant nuclei between control and MDD nuclei</td> <td>Fig 5H</td> </tr> <tr> <td>Nagy_et_al_astro_subsetting.R</td> <td>Subsetting astrocytes from the full Nagy et al snRNA-seq dataset </td> <td>NA</td> </tr> <tr> <td>Network_analysis.R</td> <td>To generate and analyse a graph of coexpression networks</td> <td>Fig 3E</td> </tr> <tr> <td>NicheNet.R</td> <td>For a NicheNet analysis to infer patterns of cell-cell communication</td> <td>Fig 6F</td> </tr> <tr> <td>Vizium_analysis.R</td> <td>Processing spatial RNA-seq data and generating cell scores for spatial inference of identified cell states</td> <td>Fig 5F</td> </tr> </tbody> </table>
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.