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10,735 results for “dependence”
BUMP: A Benchmark of Reproducible Breaking Dependency Updates
<p>Bump is a benchmark of breaking dependency updates. A breaking update is defined as a pair of commits for a Java project, which we designate as the pre-commit and the breaking-commit. When we build the project with the pre-commit, compilation and test execution are successful, while the build of the breaking-commit fails. Each breaking-commit is a one-line change in the Maven pom file.</p>
Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of a Constraint-Based Continuous Bubnov-Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures
<p>This repository holds all of the raw data generated by my (Modern) Fortran code for a paper "Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of a Constraint-Based Continuous Bubnov-Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures".</p><p>The (Modern) Fortran code solves the multi-group neutron diffusion equation using a novel IGA-based spatial discretisations.</p>
The data behind the ApJ article "Environmental Dependence of Type Ia Supernovae in Low-Redshift Galaxy Clusters"
<p>Data from "Environmental Dependence of Type Ia Supernovae in Low-Redshift Galaxy Clusters", <a href="https://ui.adsabs.harvard.edu/abs/2023arXiv230601088L/abstract">NASA ADS</a></p><p>inner_cluster_data.csv and outer_cluster_data.csv include the SALT3 mB, x1, and c parameter values, distance moduli and Hubble residuals (with _1 referring to Figure 9 and _2 referring to Figure 10), outlier designation from MCMC procedure, host cluster, host cluster redshift (with Hubble diagram version converted to frame of CMB), host cluster r500, projected separation from cluster center, NED Host galaxy name, photometrically-derived estimate for host mass, host or SN redshift used in analysis, and the Host SFR category (Q: quiescent, SF: star-forming, GV: green valley) for our cluster SNe Ia.</p><p>sf_field.csv and quiescent_field.csv contain SALT parameter values, distance moduli and Hubble residuals (from Figure 10), host galaxy sSFR and mass measurements, and host redshifts (all spectroscopic, also with Hubble diagram converted values) for SNe Ia in our field samples.</p><p>full_cluster.csv contains the data from the table in the appendix of the paper.</p><p>The inner_cluster_/outer_cluster_mcmc_samples.csv files contain the samples needed to reproduce the corner plot for Figure 10.</p><p>The Python scripts recreate the figures from the paper given the above data. The details for which columns and constraints needed to reproduce the figures are included in these files.</p>
Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of an Interior-Penalty Scheme for a Discontinuous Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures
<p>This repository holds all of the raw data generated by my (Modern) Fortran code for a paper "Energy-Dependent, Self-Adaptive Mesh h(p)-Refinement of an Interior-Penalty Scheme for a Discontinuous Galerkin Isogeometric Analysis Spatial Discretisation of the Multi-Group Neutron Diffusion Equation with Dual-Weighted Residual Error Measures".</p><p>The (Modern) Fortran code solves the multi-group neutron diffusion equation using a novel IGA-based spatial discretisations.</p>
Dataset 1 for Publication: Separation-dependent near-field effects in Mie scattering spectra of two optically trapped aerosol droplets
<p>Dataset for Publication: ASCII files of Mie spectra for each experimentally analysed run, calibrated wavelength files, and brightfield images at each interdroplet separation.</p>
A Terrylene Bisimide based Universal Host for Aromatic Guests to Derive Contact Surface-Dependent Dispersion Energies
<p>Additional data to report <a href="https://doi.org/10.1002/anie.202318451">https://doi.org/10.1002/anie.202318451</a>:<br><br>π–π interactions are among the most important intermolecular interactions in supramolecular systems. Here we determine experimentally a universal parameter for their strength that is simply based on the size of the interacting contact surfaces. Toward this goal we designed a new cyclophane based on terrylene bisimide (TBI) π-walls connected by <em>para</em>-xylylene spacer units. With its extended π-surface this cyclophane proved to be an excellent and universal host for the complexation of π-conjugated guests, including small and large polycyclic aromatic hydrocarbons (PAHs) as well as dye molecules. The observed binding constants range up to 10<sup>8</sup> M<sup>−1</sup> and show a linear dependence on the 2D area size of the guest molecules. This correlation can be used for the prediction of binding constants and for the design of new host–guest systems based on the herewith derived universal Gibbs interaction energy parameter of 0.31 kJ/molÅ<sup>2</sup> in chloroform.</p>
Additional Data: Poised PABP-RNA hubs implement signal-dependent mRNA decay in development
<p>This repository contains processed data resulting from iCLIP experiments that were analysed in the following paper:"<strong>Poised PABP-RNA hubs implement signal-dependent mRNA decay in development</strong>"<br>The paper is published at Nature Structural and Molecular BIology.</p> <h2><br>Archived data</h2> <p>Data archived in this repository include:</p> <ol> <li>Data derived from iCLIP experiments targeting LIN28A, PABPC1, and PABPC4, that were analysed in the manuscript (see iCLIP.zip). Raw data is available from ENA, with the accession code PRJEB60519. <ol> <li>Sample descriptions are given in iCLIP-SampleAnnotation.csv</li> <li>Crosslink files in BED6 format (individual replicates and merged replicates)</li> <li>Peak files generated with the Clippy peak caller in BED6 format</li> <li>K-mer enrichment around high-confidence crosslink sites in the 3'-UTRs, calculated by the PEKA software</li> </ol> </li> <li>Expression values (salmon quantfiles) for 3'-seq experiments, specified in "QuantseqExperimentsAnnotation.tsv", are available in "SalmonQuantfiles.zip". Raw data is available from ENA, with the accession code PRJEB60519.</li> <li>Source code of the nextflow pipeline, which was used on the iMaps webserver to analyse iCLIP data and produce the files archived here (see imaps-nf-0.30.zip).</li> <li>A list of naive genes, that were analysed in the manuscript (see NaiveGeneIds.csv).</li> </ol> <h2>Details on iCLIP data generation</h2> <p>iCLIP data for LIN28A-WT (in 2iL and FGF2 treated cells), LIN28A-S200A (in FGF2 treated cells) as well as for PABPC1 and PABPC4 (in LIN28A KO cells with and without LIN28A overexpression), were analysed on iMaps Goodwright server (<a href="https://imaps.goodwright.com/">https://imaps.goodwright.com/</a>). The LIN28A iCLIPs were analysed on 18th of July, 2022; the PABPC iCLIPs were analysed on 26th of December, 2022. The code and settings used in the pipeline (release v0.30) can be viewed at <a href="https://github.com/goodwright/imaps-nf">https://github.com/goodwright/imaps-nf </a>, and is also archived here - (imaps-nf-0.30.zip)<br> </p> <ul> <li>First, reads were demultiplexed using Ultraplex and barcodes were trimmed from the reads. The default Ultraplex settings were applied, as denoted below:</li> </ul> <blockquote> <p>adapter='AGATCGGAAGAGCGGTTCAG'<br>adapter2='AGATCGGAAGAGCGTCGTG'<br>barcodes='barcode.csv',<br>final_min_length=20<br>fiveprimemismatches=1<br>ignore_no_match=False<br>ignore_space_warning=False<br>inputfastq='MOD4878A1-merged.fastq.gz',<br>keep_barcode=False,<br>min_trim=3,<br>outputprefix='demux',<br>phredquality=30,<br>phredquality_5_prime=0,<br>sbatchcompression=False,<br>threads=10,<br>threeprimemismatches=0,<br>ultra=False</p> </blockquote> <p> </p> <ul> <li>TrimGalore was used to run FASTQC and quality trim the reads and remove reads with length less than 10 nt:</li> </ul> <blockquote> <p>trim_galore --fastqc --length 10 -q 20 --cores 8 --gzip file.fastq.gz</p> </blockquote> <p> </p> <ul> <li>Reads were then premapped to rRNA, tRNA sequences referred to as small RNA, smRNA, using mouse genome build (GRCm39 GENCODE M28 annotation) with Bowtie v1.3.0 (Langmead et al., 2009)</li> </ul> <blockquote> <p>bowtie --threads 12 --sam -x $INDEX -q --un file.unmapped.fastq -v 2 -m 100 --norc --best --strata file.fq.gz 2</p> </blockquote> <p> </p> <ul> <li>Reads that did not map with Bowtie were then aligned with STAR v2.7.9a (Dobin et al., 2013) to mouse genome build (GRCm39 GENCODE M28 annotation).</li> </ul> <blockquote> <p>STAR \<br>--genomeDir star \<br>--readFilesIn file.unmapped.fastq.gz \<br>--runThreadN 12 \<br>--outFileNamePrefix 1_R1. \<br>\<br>--sjdbGTFfile Homo_sapiens_filtered.gtf \<br>--outSAMattrRGline 'ID:1_R1' 'SM:1_R1' \<br> --readFilesCommand zcat --outSAMtype BAM SortedByCoordinate --quantMode TranscriptomeSAM --outFilterMultimapNmax 1 --outFilterMultimapScoreRange 1 --outSAMattributes All --alignSJoverhangMin 8 --alignSJDBoverhangMin 1 --outFilterType BySJout --alignIntronMin 20 --alignIntronMax 1000000 --outFilterScoreMin 10 --alignEndsType Extend5pOfRead1 --twopassMode Basic</p> </blockquote> <p> </p> <ul> <li>PCR-duplicates were removed using UMI-tools (Smith, Heger and Sudbery, 2017)</li> </ul> <blockquote> <p>java -jar /UMICollapse/umicollapse.jar \<br> bam \<br> -i file.Aligned.sortedByCoord.out.bam \<br> -o file.dedup.bam \<br> --umi-sep rbc:</p> </blockquote> <p> </p> <ul> <li>The nucleotide preceding each sequencing read was assigned as the crosslink event.</li> </ul> <p> </p> <ul> <li>Peaks of crosslinking signal were identified with Clippy v1.4.1, using the default settings.</li> </ul> <p> </p> <ul> <li>Obtained peaks and crosslink sites were used to run PEKA v1.0.0 (Kuret et al., 2022), using the default settings.</li> </ul> <p> </p> <ul> <li>For Clippy and PEKA, the GENCODE primary assembly annotation M28 was filtered to retain only entries with transcript support level 1 or 2, in genes where such transcripts were available, and used to produce a segmentation file with the <em>get_segments</em> function from the iCount tool (Curk, 2019).</li> </ul> <p> </p> <ul> <li>All files generated during data processing are available from the iMaps Goodwright webserver for analysis of CLIP data (see <a href="https://imaps.goodwright.com/collections/882635250203/">https://imaps.goodwright.com/collections/882635250203/</a> and <a href="https://imaps.goodwright.com/collections/340215254997/">https://imaps.goodwright.com/collections/340215254997/</a> for LIN28A and PABPC1/4 iCLIPs, respectively).</li> </ul> <h2>Source data</h2> <p>Raw sequencing reads, from which the data enclosed here were derived, are accessible at ENA (PRJEB60519).<br>The raw sequencing reads and all data produced by the analysis pipeline is also available at the iMaps webserver (see <a href="https://imaps.goodwright.com/collections/882635250203/">https://imaps.goodwright.com/collections/882635250203/</a> and <a href="https://imaps.goodwright.com/collections/340215254997/">https://imaps.goodwright.com/collections/340215254997/</a> for LIN28A and PABPC1/4 iCLIPs, respectively); and on the updated Flow webserver (see <a href="https://app.flow.bio/projects/882635250203/">https://app.flow.bio/projects/882635250203/</a> and <a href="https://app.flow.bio/projects/340215254997/">https://app.flow.bio/projects/340215254997/ </a>for LIN28A and PABPC1/4 iCLIPs, respectively).</p> <h2>Downstream computational analysis of enclosed data</h2> <p>The code, used to analyse the data enclosed here and train the CNN to predict transcript stability in naive-to-primed transition based on 3'UTR nucleotide sequence, is available at GitHub (<a href="https://github.com/ulelab/LIN28A_RNPreassembly_bioinformatics">https://github.com/ulelab/LIN28A_RNPreassembly_bioinformatics</a>) and archived on Zenodo (<a href="../doi/10.5281/zenodo.10054297">https://zenodo.org/doi/10.5281/zenodo.10054297</a><strong>).</strong></p>
Analysis of variant-dependent m6A modifications within the Human genome
<p>Interactive and machine-readable results produced by the <a href="https://github.com/cumbof/m6Ad-SNVs" target="_blank" rel="noopener">m6Ad-SNVs</a> tool to asses if m6A-distal SNVs affect DRACH site accessibility, specifically by evaluating the alteration of base-pairing of nucleotides within segments of the DRACH motif.</p> <p>These results contain the predicted m6Ad-SNV candidates with the length of the reference and m6Ad-SNV-containing alternate sequences limited to 250 base pairs. This constraint has been applied to maintain the reliability of the results predicted by RNAFold (<a href="https://www.tbi.univie.ac.at/RNA/">ViennaRNA</a> package). The sequence composition contains up to 100 base pairs from 3'UTRs, with the remaining base pairs limited to the last two exons.</p>
CO2-dependence of Longwave Clear-sky Feedback is sensitive to Temperature [Dataset]
<p>These data are simulated from PyRads (https://github.com/danielkoll/PyRAD) and are used to plot figures in our study.</p>
Chronic Ethanol Exposure Produces Sex-Dependent Impairments in Value Computations in the Striatum
<div> <div>These datasets and scripts are organized by figures. All data are stored as .mat format and can be open and manipulated using MATLAB. Scripts are all written in MATLAB and can be ran in MATLAB.</div> <div>There are two ways to run the code to reproduce each figures and statistics.</div> <div>1. Run RUN_ME.m. In this case, the file will automatically excute scripts to load corresponding data and figures.</div> <div>2. Open individual script to load corresponding data and generate statistics and figures.</div> <br> <div>All scripts here have been validated and tested. The system and coding environment is:</div> <div>- Windows 11 24H2</div> <div>- MATLAB 2023a</div> <br> <div>Matlab dependent package (not all are required but those are installed in my environment):</div> <div>- Bioinformatics Toolbox v4.17</div> <div>- Communications Toolbox v8.0</div> <div>- Computer Vision Toolbox v10.4</div> <div>- Curve Fitting Toolbox v3.9</div> <div>- Data Acquisition Toolbox v4.7</div> <div>- Database Toolbox v11.0</div> <div>- Deep Learning HDL Toolbox v1.5</div> <div>- Deep Learning Toolbox v14.6</div> <div>- DSP HDL Toolbox v1.2</div> <div>- Econometrics Toolbox v6.2</div> <div>- Financial Toolbox v6.5</div> <div>- Fixed-point Designer v7.6</div> <div>- Image Processing Toolbox v11.7</div> <div>- MATLAB Coder v5.6</div> <div>- MATLAB Compiler v8.6</div> <div>- MATLAB Compiler SDK v7.2</div> <div>- MATLAB Report Generator v5.14</div> <div>- MATLAB Support for MinGW-w64 C/C++ Compiler v23.1.0</div> <div>- Optimization Toolbox v9.5</div> <div>- Parallel Computing Toolbox v9.5</div> <div>- FR Toolbox v4.5</div> <div>- Signal Integrity Toolbox v1.3</div> <div>- Simulink v10.7</div> <div>- Statistics and Machine Learning Toolbox v12.5</div> <div>- Symbolic Math Toolbox v9.3</div> <div>- Text Analytics Toolbox v1.10</div> <div>- Wavelet Toolbox v6.3</div> </div>
Supplemental data for "Intramolecular feedback regulation of the LRRK2 Roc G domain by a LRRK2 kinase dependent mechanism" (Gilsbach et al., eLife 2024, doi:10.7554/eLife.91083)
<p><strong>Supportive data for the eLife version of record.</strong></p> <p><strong>(1) Data used for the Michaelis Menten Kinetics.</strong></p> <p><strong>HPLC-based assay.</strong> Steady-state kinetic measurements of LRRK2-mediated GTP hydrolysis were performed as previously described (Ahmadian et al., 1997). Briefly, 0.1 µM of full-length LRRK2 was incubated with different amounts of GTP (0, 25, 75, 150, 250, 500, 1000, 2000, 3000 and 5000 µM) and production of GDP was monitored by reversed phase C18 HPLC. To this end, the samples (10 µl) were directly injected on a reversed-phase C18 column (pre-column: Hypersil Gold, 3µm particle size, 4.6x10mm; main column: Hypersil Gold, 5µm particle size, 4.6x250mm, Thermo Scientific) using an Ultimate 3000 HPLC system (Thermo Scientific, Waltham, MA, USA) in HPLC-buffer containing 50 mM KH<sub>2</sub>PO<sub>4</sub>/K<sub>2</sub>HPO<sub>4</sub> pH 6.0, 10 mM tetrabutylammonium bromide and 10-15% acetonitrile. Subsequently, samples were analyzed using the HPLC integrator (Chromeleon 7.2, Thermo Scientific, Waltham, MA, USA). Initial rates of GDP production were plotted against the GTP concentration using GraFit5 (v.5.0.13, Erithacus Software). The number of experiments is indicated in the graph and data point is the average (±s.e.m.) of indicated repetitions. The Michaelis-Menten equation was fitted to determine K<sub>M</sub> (±s.e.) and k<sub>cat</sub> (±s.e.). Excel sheets used for the calculation of means are provided. No values are reported if the HPLC separation failed (e.g. unstable baseline).</p> <p><strong>Charcoal GTP hydrolysis assay. </strong>The [γ-32P]GTP charcoal assay was performed as previously described (Bollag and McCormick, 1995). Briefly, 0.1 µM full-length LRRK2 or 0.5 µM 6xHIS-MBP-RocCOR was incubated with different GTP concentrations, ranging from 75 µM to 8 mM, in the presence of [γ-<sup>32</sup>P] GTP in GTPase assay buffer (30 mM Tris pH 8, 150 mM NaCl, 10 mM MgCl<sub>2</sub>, 5% (v/v) Glycerol and 3 mM DTT). Samples were taken at different time-points and immediately quenched with 5% activated charcoal in 20 mM phosphoric acid. All non-hydrolyzed GTP and proteins were stripped by the activated charcoal and sedimented by centrifugation. The radioactivity of the isolated inorganic phosphates was then measured by scintillation counting. The initial rates of γ-phosphate release and the Michaelis-Menten kinetics were calculated as described above.</p> <p><strong>(2) Profile plots (Raw data) obtained for the Mass photometry analysis for T1343A vs WT LRRK2.</strong></p> <p>MP was performed as described in (Guaitoli et al., 2023).<strong> </strong>Briefly, the dimer ratio of LRRK2 was determined on a Refeyn Two MP instrument (Refeyn). Prior to the experiment, a standard curve relating particle contrasts to molecular weight was established using a Native molecular weight standard (Invitrogen, 1:200 dilution in HEPES-based elution buffer: 50 mM HEPES [pH 8.0], 150 mM NaCl supplemented with 200 µM desthiobiotin). Prior to mass photometry, the proteins, either WT or T1343A LRRK2, were incubated with 0.5 mM ATP or buffer (control) for 30 min at 30 ℃. The LRRK2 protein was diluted to 2x of the final concentration (end concentrations: 75 nM and 100 nM) in elution buffer. The optical setup was focused in 10 μl elution buffer before adding 10 µl of the adjusted protein sample. Depending on the obtained count numbers, acquisition times were chosen between 20 s to 1 min. The dimer ratio in each measurement was normalize according to the equation. The measurement was perfomed in triplicates.</p> <p><strong>(3) AlphaFold3 model of LRRK2-pT1343 either bound to GDP/Mg or GTP/Mg.</strong></p> <p>Using AlphaFold3 (Abramson et al., 2024), we modeled and compared the GDP vs the GTP-state of phospho-T1343 LRRK2. Interestingly, the AlphaFold3 model suggests, that the phosphate group of the pT1343 residue is orientated inwards thereby substituting the gamma phosphate of the GTP in the GDP-bound state of LRRK2. This finding is in well agreement with MD simulations published recently (Stormer et al., 2023).</p> <p><strong>(4) Western blot RAW files for the cell-based phospho Rab asssay (RAW data for Figure 6 supplement 2/ Supplemental Figure 4 in the preprint version, Gilsbach et al, 2024)</strong></p> <p>Cell-based LRRK2 activity assays were performed as previously described (Singh et al., 2022). Briefly,<strong> </strong>HEK293T cells were cultured in DMEM (supplemented with 10% Fetal Bovine Serum and 0.5% Pen/Strep). For the assay, the cells were seeded onto six-well plates and transfected at a confluency of 50-70% with SF-tagged LRRK2 variants using PEI-based lipofection. After 48 hours cells were lysed in lysis buffer [30 mM Tris-HCl (pH7.4), 150 mM NaCl, 1% NonidentP-40 substitute, complete protease inhibitor cocktail, PhosStop phosphatase inhibitors (Roche)]. Lysates were cleared by centrifugation at 10,000 x g and adjusted to a protein concentration of 1 µg/µl in 1x Laemmli Buffer. Samples were subsequently subjected to SDS PAGE and Western Blot analysis to determine LRRK2 pS935 and Rab10 T73 phosphorylation levels, as described below. Total LRRK2 and Rab10 levels were determined as a reference for normalization. For Western blot analysis, protein samples were separated by SDS–PAGE using NuPAGE 10% Bis-Tris gels (Invitrogen) and transferred onto PVDF membranes (Thermo Fisher). To allow simultaneous probing for LRRK2 on the one hand and Rab10 on the other hand, membranes were cut horizontally at the 140 kDa MW marker band. After blocking non-specific binding sites with 5% non-fat dry milk in TBST (1 h, RT) (25 mM Tris, pH 7.4, 150 mM NaCl, 0.1% Tween-20), membranes were incubated overnight at 4°C with primary antibodies at dilutions specified below. Phospho-specific antibodies were diluted in TBST/ 5% BSA (Roth GmbH). Non-phospho-specific antibodies were diluted in TBST/ 5% non-fat dry milk powder (BioRad). Phospho-Rab10 levels were determined by the site-specific rabbit monoclonal antibody anti-pRAB10(pT73) (Abcam, ab230261) and LRRK2 pS935 was determined by the site-specific rabbit monoclonal antibody UDD2 (Abcam, ab133450), both at a dilution of 1:2,000. Total LRRK2 levels were determined by the in-house rat monoclonal antibody anti-pan-LRRK2 (clone 24D8; 1:10,000) (Carrion et al., 2017). Total Rab10 levels were determined by the rabbit monoclonal antibody anti-RAB10/ERP13424 (Abcam, ab181367) at a dilution of 1:5,000. For detection, goat anti-rat IgG or anti-rabbit IgG HRP-coupled secondary antibodies (Jackson ImmunoResearch) were used at a dilution of 1:15,000 in TBST/ 5% non-fat dry milk powder. Antibody–antigen complexes were visualized using the ECL plus chemiluminescence detection system (GE Healthcare) using the Stella imaging system (Raytest) for detection and quantification.</p> <p><strong>Figure 6 Source Data 1:</strong> <span>Images generated by the Stella system are shown which were used for quantification. The annotation file equals Figure6-figure supplement 2 (Gilsbach et al., eLife 2024, doi:10.7554/eLife.91083). The lines corresponding to </span>LRRK2 pS935, total LRRK2, Rab10 pT73 and total Rab10 were <span>used for the quantification shown in Figure 6.</span></p>
Dataset: Environment effects on X-ray absorption spectra with quantum embedded real-time Time-dependent density functional theory approaches
<p>This dataset collects the outputs from real-time TDDFT simulation of X-ray absorption of halides in model systems, using the frozen density embedding (FDE) and block-orthogonalized Manby-Miller embedding (BOMME), as well as processing tools and scripts used to carry out the calculations.</p>
Kobalt: Extension Corpus and Annotation Guidelines for Verb Classification and Dependency Adjustments
<p>Kobalt (Zinsmeister et al. 2012) is a task-based corpus of essays written by learners and native speakers of German. This repository contains data that was not included in the original corpus and new layers of annotation to the original and the extended corpus, specifically morphological and syntactic classification of verbs and corrections and changes to dependency parses. Please refer to the annotation guidelines included in this repository for further information.<br> </p>
Dataset for Number agreement and dependency length in Finnish dialects
<p>This material contains the dataset and the scripts from the <a href="https://version.helsinki.fi/gramadapt/depling2021-number-agreement">gitlab repository</a> of the following article. Please cite the article when using the data.</p> <p>Sinnemäki, Kaius & Akira Takaki 2021. Number agreement, dependency length, and word order in Finnish traditional dialects. In <em>Proceedings of the Sixth International Conference on Dependency Linguistics (Depling, SyntaxFest 2021)</em>. Stroudsburg, PA: The Association for Computational Linguistics.</p> <p> </p>
Cysteine dependence of Lactobacillus iners is a potential therapeutic target for vaginal microbiota modulation
<p>Compressed directories containing code and data files sufficient to reproduce analysis from Bloom et al paper on <em>Lactobacillus iners</em> (<em>Nature Microbiology</em>). An earlier, non-peer-reviewed manuscript version containing largely the same analysis was posted as a pre-print in <em>bioRxiv</em> at (https://doi.org/10.1101/2021.06.12.448098). Three compressed directory for analyses of:</p> <ol> <li>Vaginal <em>Lactobacillus </em>genome catalog characterization and gene content analysis.</li> <li>Analysis of relationship between cervicovaginal microbiota composition and cysteine concentrations in vaginal fluid from a South African cohort</li> <li>Analysis of results of <em>in vitro </em>mixed culture competition assays including: <ol> <li>Pairwise competition between <em>L. iners</em> and <em>Lactobacillus crispatus</em> in <em>Lactobacillus</em> MRS broth containing L-cysteine +/- S-methyl-L-cysteine (SMC)</li> <li>Defined bacterial-vaginosis (BV)-like communities including <em>L. iners</em>, <em>L. crispatus</em>, and BV-associated species <em>Gardnerella vaginalis</em>, <em>Prevotella bivia</em>, and <em>Atopobium (Fannyhessea) vaginae</em> cultured in S-broth with or without SMC and/or metronidazole.</li> </ol> </li> </ol>
A Dataset for detecting Change Coupling and Structural Dependencies.
<p><strong>Introduction</strong></p> <p>This repository hosts the results of runs evaluating the first developments of prototype tool for detecting Change Coupling and Structural Dependencies in the context of cyber-physical-systems(CPS). This tool analyzes the projects' code repository history and uses rule mining to detect logistical couplings, it also analyzes the source code to detect which of those changes have additional structural dependencies. </p> <p> </p> <p>The two datasets are the result from the analysis of two popular GitHub CPS projects:</p> <ul> <li>Eclipse Concierge (Java) — a small-footprint implementation of the OSGi Core Specification optimized for mobile and embedded devices.<sup>1</sup></li> <li>PX4 — a flight control solution for drones, that also contains a Drone Middleware Platform, providing drivers and middleware to run drones <sup>2</sup></li> </ul> <p>The datasets were generated by running a change coupling and structural dependencies analyzer, that is a prototype under development. The resulting raw data is available under the <code>project_results/Proj_Name</code> folder. Additionally, in the folder <code>notebooks </code>the user can find examples of how to easily query the functionality offered by the visualization and analytics libraries (in folder <code>analytics)</code>.</p> <p> </p> <p>[1] https://github.com/eclipse/concierge</p> <p>[2] https://github.com/PX4/PX4-Autopilot</p>
Reversible Pressure-Dependent Mechanochromism of Dion–Jacobson and Ruddlesden–Popper Layered Hybrid Perovskites
<p>Structural, optoelectronic, and supplementary characterization data for "Reversible Pressure-Dependent Mechanochromism of Dion–Jacobson and Ruddlesden–Popper Layered Hybrid Perovskites" DOI: doi.org/10.1002/adma.202108720</p> <ul> <li>Dataset.zip: Data described in the main text and in the SI organised by figure number. Dataset are provided in .xy, .csv, .data and Excel (*.xlsx) file format.</li> <li>Figures.zip: figures described in the main text and in the SI in .png and .pdf</li> <li>readme.txt: replication packages information</li> </ul> <p> </p>
Sample accession list for "Malaria protection due to sickle haemoglobin depends on parasite genotype"
<p>This dataset contains a list of sample accessions and associated metadata for <em>P.falciparum</em><br> DNA samples sequenced for the analysis presented in the paper:</p> <p><strong>Malaria protection due to sickle haemoglobin depends on parasite genotype</strong></p> <p>Gavin Band, Ellen M. Leffler, Muminatou Jallow, Fatoumatta Sisay-Joof, Carolyne<br> M. Ndila, Alexander W. Macharia, Christina Hubbart, Anna E. Jeffreys, Kate Rowlands, Thuy<br> Nguyen, Sónia Gonçalves, Cristina V. Ariani, Jim Stalker, Richard D. Pearson, Roberto<br> Amato, Eleanor Drury, Giorgio Sirugo, Umberto d'Alessandro, Kalifa A. Bojang, Kevin<br> Marsh, Norbert Peshu, Joseph W. Saelens, Mahamadou Diakité, Steve M. Taylor10, David J.<br> Conway, Thomas N. Williams, Kirk A. Rockett, Dominic P. Kwiatkowski</p> <p>Nature (2021) doi: <a href="https://doi.org/10.1038/s41586-021-04288-3">10.1038/s41586-021-04288-3</a> <strong>bioRxiv link</strong>: <a href="http://doi.org/10.1101/2021.03.30.437659">doi.org/10.1101/2021.03.30.437659</a>.</p> <p>The data contains: i. a single tab-delimited text file containing accessions and sequence read quality control-related information related to the processing described in [1], and ii. a README file describing the contents of the data in markdown and HTML format. Please see the enclosed README file for full details.</p> <p>A full list of datasets which have been released with this manuscript can be found on the <a href="https://www.malariagen.net/resource/32">MalariaGEN website</a>.</p> <p> </p>
Reversible Pressure-Dependent Mechanochromism of Dion–Jacobson and Ruddlesden–Popper Layered Hybrid Perovskites
<p>Structural, optoelectronic, and supplementary characterization data for “Reversible Pressure-Dependent Mechanochromism of Dion–Jacobson and Ruddlesden–Popper Layered Hybrid Perovskites”, DOI:10.1002/adma.202108720.</p> <ul> <li>Dataset.zip: Data described in the Figures of the main text and Supporting Information as .xy, .data, .csv, .xlsx, and .asc files</li> <li>Figures.zip: Figures provided in the main text and Supporting Information as .pdf and .png files.</li> </ul>
Litter decomposition is moderated by scale-dependent microenvironmental variation in tundra ecosystems
<p><strong>QHI_crop.tiff </strong>= We carried out topographic surveys using unoccupied aerial vehicles photogrammetry in August 2017. We used three UAV platforms to collect RGB multispectral data at a fine (3 cm) spatial resolution: DJI Phantom 4 Pro and Advanced (multicopter), and Phantom FX-61 (fixed wing), and used used structure from motion with multiview steriopsis to obtain a fine-grain 10 cm spatial resolution digital surface model and orthomosaic as described in Cunliffe et al. (2019a, 2019b).</p> <p><strong>thermsum.tif </strong>= We used the microclima package in R (Kearney et al., 2020; Maclean et al., 2019) to model surface air temperature at a 1-m spatial grain. Using our fine resolution DSM, we modelled mean surface temperatures at the study site for each day spanning the teabag burial period of 13th July to 9th August 2017. The microclima model incorporates local daily climate, radiation, cloud cover and coastal exposure data from gridded global datasets derived from RCNEP (<a href="https://www.zotero.org/google-docs/?broken=Zl6wgI">Kemp et al., 2012)</a>. We summed the 28 TIF files produced through this modelling technique to produce a 28-day thermal sum variable - a metric which captures the overall heating of the ground surface over the course of the experiment.</p> <p><strong>Cited Works:</strong></p> <p> </p> <p>Cunliffe, A., I. Myers-Smith. J. Kerby and W. Palmer (2019a). Orthomosaic of permafrost landscape on Qikiqtaruk – Herschel Island, Yukon, Canada: August 2017. NERC Polar Data Centre. DOI:10.5285/29bf1c9f-a39a-452c-b9f9-de35d9fb9179.</p> <p> </p> <p>Cunliffe, A., G. Tanski, B. Radosavljevic, W. Palmer, T. Sachs, H. Lantuit, J. Kerby, and I. Myers-Smith (2019b) Rapid retreat of permafrost coastline observed with aerial drone photogrammetry. The Cryosphere 13(5):1513-1528. DOI: 10.5194/tc-13-1513-2019.</p> <p> </p> <p><a href="https://www.zotero.org/google-docs/?hjdBYY">Maclean, I. M. (2020). Predicting future climate at high spatial and temporal resolution. <em>Global Change Biology</em>, <em>26</em>(2), 1003–1011.</a></p> <p> </p> <p>Kearney, M. R., Gillingham, P. K., Bramer, I., Duffy, J. P., & Maclean, I. M. (2020). A method for computing hourly, historical, terrain‐corrected microclimate anywhere on Earth. <em>Methods in Ecology and Evolution</em>, <em>11</em>(1), 38-43.</p> <p> </p> <p>Kemp, M. U., Van Loon, E. E., Shamoun-Baranes, J., & Bouten, W. (2012). RNCEP: global weather and climate data at your fingertips. <em>Methods in Ecology & Evolution</em>, <em>3</em>(1), 65-70.</p> <p><strong>Paper Abstract:</strong></p> <ol> <li> <p><strong>The Arctic tundra is one of the world’s largest organic carbon stores, yet this carbon is vulnerable to accelerated decomposition as climate warming progresses. We currently know very little about landscape-scale controls of litter decomposition in tundra ecosystems, which hinders our understanding of the global carbon cycle. </strong></p> </li> <li> <p><strong>Here, we examined how local-scale topography, surface air temperature, soil moisture and permafrost conditions influenced litter decomposition rates across a heterogeneous tundra landscape on Qikiqtaruk - Herschel Island (Yukon, Canada).</strong></p> </li> <li> <p><strong>We used the Tea Bag Index protocol to derive decomposition metrics which we then compared across environmental gradients, including thermal sum surface temperature data derived from fine-resolution microclimate data modelled from drone derived topographic data.</strong></p> </li> <li> <p><strong>We found greater green tea litter mass loss and faster decomposition rates in wetter and warmer areas within the landscape, and to a lesser extent in areas with deeper permafrost active layer thickness.</strong></p> </li> <li> <p><strong>Spatially heterogeneous belowground conditions (soil moisture and active layer depth) explained variation in decomposition metrics at the landscape-scale (> 10 m) better than surface temperature.</strong></p> </li> <li> <p><strong>Surprisingly, there was no strong control of elevation or slope of litter decomposition. We also found higher decomposition rates on North-facing relative to South-facing aspects at microsites that were wetter rather than warmer.</strong></p> </li> </ol>
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.