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636 results for “Chimerism”
Data set for the journal article: Site-Specific Protein Ubiquitylation Using an Engineered, Chimeric E1 Activating Enzyme and E2 SUMO Conjugating Enzyme Ubc9
<p>Mutations observed in evolved chimeric E1 variants. Top row (1.X to 4.X) describes rounds of evolutions with respective variants in the round. </p> <p>Residues that appear to be enriched are highlighted with gray fill. Star (★) marks residues subjected to saturation mutagenesis in the round 4.</p>
Tracking Down Chimeric Assemblies In The TrackIt DNA Ladder Using Nanopore Sequencing
<h2>Dataset Description</h2><p>These files represent two different LSK114 sequencing runs on a TrackIt 1kb Plus DNA Ladder sample, and associated data analysis.</p><ul><li>July 20 2023 Flongle Run (191 Mb; 465k reads)<ul><li>pod5_files_2023-Jul-20_DAE_DNA_Ladder.tar.gz<br>- raw POD5 format files</li><li>called_2023-Jul-20_DAE_DNA_Ladder_duplex.bam<br>- duplex called reads, called using dorado v4.0 with the 2023-09-22 bacterial methylation model</li><li>sequence_QC_2023-Jul-20_DAE_DNA_Ladder.pdf<br>- sequence length / quality QC plots</li><li>LAST_2023-Jul-20_DAE_DNA_Ladder_reads_vs_reference.tar.gz<br>- Alignment summary statistics from LAST mapping of reads to their associated reference</li><li>lengths_summary_2023-Jul-20_DAE_DNA_Ladder.txt<br>- Length / QC summary statistics</li></ul></li><li>October 12 2023 P2 Solo Run (1.95 Gb, 1.11M reads)<ul><li>pod5_files_2023-Oct-12_DNA-Ladder-1kbplus_fail.tar.gz<br>- raw POD5 format files (all failed reads)</li><li>pod5_files_2023-Oct-12_DNA-Ladder-1kbplus_pass_000-059.tar.gz<br>- raw POD5 format files (passed reads, bundle #000-059)</li><li>pod5_files_2023-Oct-12_DNA-Ladder-1kbplus_pass_060-119.tar.gz<br>- raw POD5 format files (passed reads, bundle #060-119)</li><li>pod5_files_2023-Oct-12_DNA-Ladder-1kbplus_pass_120-179.tar.gz<br>- raw POD5 format files (passed reads, bundle #120-179)</li><li>pod5_files_2023-Oct-12_DNA-Ladder-1kbplus_pass_180-222.tar.gz<br>- raw POD5 format files (passed reads, bundle #180-222)</li><li>called_2023-Oct-12_DNA-Ladder-1kbplus_duplex.bam<br>- duplex called reads [October 12, 2023], called using dorado v4.0 with the 2023-09-22 bacterial methylation model</li><li>sequence_QC_2023-Oct-12_DNA-Ladder-1kbplus.pdf<br>- sequence length / quality QC plots</li><li>LAST_2023-Oct-12_DNA-Ladder-1kbplus_reads_vs_reference.tar.gz<br>- Alignment summary statistics from LAST mapping of reads to their associated reference</li><li>lengths_summary_2023-Oct-12_DNA-Ladder-1kbplus.txt<br>- Length / QC summary statistics</li><li>ladder_seqs.fa<br>- assembled DNA ladder sequences, based on simplex reads</li></ul></li></ul><h3>Methods</h3><h3>Sample preparation</h3><p>Preparation of DNA for sequencing was carried out following the ONT Ligation Sequencing DNA V14 (SQK-LSK114) protocol, with modifications to exclude DNA repair, and keeping the sample in the same 1.5ml tube to reduce sample loss.</p><h4>Tris-buffered Saline (TBS) buffer preparation</h4><ol><li>1M stock of NaCl was made by adding 2.922g of NaCl into a 50 ml Falcon tube, then made up to 50 ml with MilliPore water</li><li>A 50 mM TBS stock was created by adding 750 μl 1M NaCl solution to a 15 ml Falcon tube, then made up to 15 ml using Qiagen Elution Buffer (EB, i.e. 10 mM Tris-HCl at pH 8.0)</li><li>The pH was confirmed to be 7.9-8.1 using a pH indicator strip (e.g. MColorpHast 6.5 - 10.0; MER1095430001)</li></ol><h4>End prep</h4><ol><li>1 μg DNA ladder (i.e. 10 μl of 0.1 μg / μl DNA ladder) was transferred into a 1.5ml Eppendorf DNA LoBind tube</li><li>The volume was topped up to 43.5 μl with TBS (i.e. 33.5 μl TBS)</li><li>3.5 μl Ultra II End-prep Reaction Buffer and 3 μl Ultra II End-prep Enzyme Mix was added</li><li>After mixing by gentle pipetting, the mixture was incubated at RT for 5 minutes, then 65 \degrees for 5 minutes</li></ol><h4>Bead cleanup</h4><ol><li>The mixture was combined with 60 μl Ampure XP beads, and incubated on a rotator mixer at RT for 5 minutes</li><li>The tube was transferred to a magnetic rack [https://www.printables.com/model/532085-open-walled-magnetic-rack]</li><li>After the supernatant became clear and colourless, supernatant was pipetted off</li><li>The magnetic beads were washed twice with 150 μl of an 80% ethanol solution</li><li>The sample was dried briefly for 30s, then eluted in 60 μl TBS</li></ol><h4>Adapter ligation and final bead cleanup</h4><ol><li>To the sample tube was added 25 μl ONT Ligation buffer (LNB), 5μl NEBNext Quick T4 DNA Ligase (reduced from the protocol-suggested 10μl because that was all that was left in the tube), and 5μl ONT Ligation Adapter (LA)</li><li>The tube was mixed by gentle pipetting, spun down for 1-3s on a mini centrifuge, then incubated for 10 minutes at RT</li><li>The mixture was combined with 40 μl Ampure XP beads (100μl Ampure XP beads were used for the Flongle sample), and incubated on a rotator mixer at RT for 5 minutes</li><li>The tube was transferred to a magnetic rack [https://www.printables.com/model/532085-open-walled-magnetic-rack]</li><li>After the supernatant became clear and colourless, supernatant was pipetted off</li><li>The magnetic beads were washed twice with 250 μl of ONT Long Fragment Buffer (LFB) for the P2 Solo run, and 250μl ONT Short Fragment Buffer <br>(SFB) for the Flongle run</li><li>The sample was dried briefly for 30s, then eluted for 10 minutes at 37 \degrees in 15 μl ONT Elution buffer (EB)</li></ol><h4>Addition of sequencing library buffers</h4><ol><li>A flow cell was prepared by flushing with ONT Flow Cell Flush (FCF) mixed with ONT Flow Cell Tether (FCT). For the P2 Solo, I used 500 μl of a 1170μl FCF solution that had 30μl FCT added to it; for the Flongle I used 60μl of a 117μl FCF solution that had 3 μl FCT added to it</li><li>1 μl of the eluted library was quantified on a Quantus Fluorometer, and approximately 50 fmol (assuming 1kb average length) was transferred to a new 1.5μl tube</li><li>For the P2 Solo run, the volume was topped up to 32 μl TBS; for the Flongle run, the volume was topped up to 12 μl TBS</li><li>To the sample tube was added ONT Sequencing Buffer (SB; P2 Solo - 100μl; Flongle - 30μl) and ONT Library Beads (LIB; P2 Solo - 68μl; Flongle - 20μl)</li><li>The flow cell was re-flushed with additional FCF/FCT mixture (500 μl for the P2 Solo; 30 μl for the Flongle)</li><li>The sequencing library was then added to the flow cell (200 μl for the P2 Solo; 30 μl for the Flongle)</li><li>The prepared flow cell was left for 10 minutes to allow the library to settle before starting sequencing</li></ol><h4>DNA Sequencing and basecalling</h4><ol><li>Sequencing was carried out using MinKNOW v23.04.6, sequencing in fast mode at 400 bases per second with a 20bp minimum sequence length and <br>5 kHz sampling rate, with reads output as POD5 files</li><li>The Flongle flow cell was run for a full standard run length (24h), whereas the PromethION flow cell was run for 1.5 hours (after which the <br>counts of 15kb reads exceeded 200)</li><li>Sequenced reads were recalled in standard (simplex) mode using Dorado v0.4.0 and the 2023-09-22 bacterial methylation model [res_dna_r10.4.1_e8.2_400bps_sup@2023-09-22_bacterial-methylation]</li></ol><h3>Bioinformatics Analysis of Ladder Sequences </h3><h4>Sequence assembly</h4><p>Assembly process for bands that are 3k in length and greater (done on LFB-depleted P2 Solo sequences): </p><ol><li>Filter >q20 reads for a 100bp region around the target length (e.g. 4950-5050bp for the 5k band) [High quality reads were not sufficient for the 15kb band; all reads were needed]</li><li>Chop the reads up with a 1000bp overlap (e.g. 3000bp for the 5k band). This works around a Canu expectation that any read overlaps should be less than X% of the read.</li><li>Assemble the reads with Canu v2.2 [#REF], treating them as "pacbio" reads (for correction and homopolymer compression), with the GenomeSize parameter set to the expected band length (e.g. GenomeSize=5000).</li><li>Extract the first reported assembled contig.</li><li>Map the contig to the nanopore adapter sequences, and trim to exclude any matching sequence.</li></ol><p>[Canu has a default genome size and read length cutoff of 1kb, and performs poorly on sequences shorter than this] <br><br>Assembly process for bands under 3k in length (done on LFB-depleted P2 Solo sequences):</p><ol><li>Filter >q20 reads for a 100bp region around the target length (e.g. 4950-5050bp for the 5k band) [High quality reads were not in sufficient abundance for the 100bp band; all reads were needed]</li><li>Assemble using a<a href="https://gitlab.com/gringer/bioinfscripts/-/blob/master/fastx-kassembler.pl"> kmer-based de-bruijn assembler</a>, trimming off low-count kmers</li><li>Extract the first reported trimmed assembled chain</li><li>Map the assembled chain to the nanopore adapter sequences, and trim to exclude any matching sequence</li><li>Use web BLASTn [#REF] to help trim any additional trailing non-matching sequence</li></ol><h4>Mapping</h4><ol><li>Use a <a href="https://gitlab.com/gringer/bioinfscripts/-/blob/master/fastx-kmapper.pl">kmer-based lightweight mapper</a> to map reads to assembled bands</li><li>Created LAST mismatch matrix using `last-train` on the 5k reads together, using the full assembled ladder sequences as a reference:<br>last-train -Q 1 ladder_seqs.fa 5k_reads.fq.gz</li><li>Mapped all reads to the assembled ladder sequences (only the reference corresponding to the most likely band source), retaining (for each read) the mapping that had the longest combined proportion of read and reference sequence mapped:<br>lastal -p bacterial.mat -P 10 ladder_seqs.fa reads_2023-Oct-12_DNA-Ladder-1kbplus_called_all.fq.gz | \ <br> ~/scripts/maf2csv.pl | \ <br> awk -F ',' '{print $0","($8/100 * $13/100)}' | \ <br> sort -t ',' -k 16rg,16 | sort -t ',' -k 1,1 -u | sort -t ',' -k 1r,1 | \ <br> perl -pe 's/,[^,]*$/\n/' > LAST_reads_vs_ladder_longestMatch.csv.gz</li></ol>
Electrochemical data plotted in A. Fasano, C. Guendon, A. Jacq-Bailly, A. Kpebe, J. Wozniak, C. Baffert, M. del Barrio, V. Fourmond, M. Brugna, C. Léger , « A chimeric NiFe hydrogenase heterodimer to assess the role of the electron transfer chain in tuning the enzyme's catalytic bias and oxygen tolerance », J. Am. Chem. Soc. 145, 36, 20021–20030 (2023). doi: 10.1021/jacs.3c06895
<p>Text file of all the electrochemical data shown in the following paper: A. Fasano, C. Guendon, A. Jacq-Bailly, A. Kpebe, J. Wozniak, C. Baffert, M. del Barrio, V. Fourmond, M. Brugna, C. Léger , « A chimeric NiFe hydrogenase heterodimer to assess the role of the electron transfer chain in tuning the enzyme's catalytic bias and oxygen tolerance », J. Am. Chem. Soc. 145, 36, 20021–20030 (2023). <a href="dx.doi.org/10.1021/jacs.3c06895" target="_blank" rel="noopener">doi: 10.1021/jacs.3c06895</a></p>
Study of Japanese Encephalitis Chimeric Virus Vaccine (JE-CV) in Children Previously Immunized With JE-CV
ClinicalTrials.gov study NCT01190228. IPD Sharing: YES. Countries: 1. Publications: 1.
Study of a Single Primary Dose Live Attenuated Japanese Encephalitis Chimeric Virus Vaccine (IMOJEV®) in Healthy Subjects
ClinicalTrials.gov study NCT02492165. IPD Sharing: YES. Countries: 1. Publications: 1.
Data from: Non-clonal coloniality: genetically chimeric colonies through fusion of sexually produced polyps in the hydrozoan Ectopleura larynx
Hydrozoans typically develop colonies through asexual budding of polyps. Although colonies of Ectopleura are similar to other hydrozoans in that they consist of multiple polyps physically connected through continuous epithelia and shared gastrovascular cavity, Ectopleura larynx does not asexually bud polyps indeterminately. Instead, after an initial phase of limited budding in a young colony, E. larynx achieves its large colony size through the aggregation and fusion of sexually (non-clonally) produced polyps. The apparent chimerism within a physiologically integrated colony presents a potential source of conflict between distinct genetic lineages, which may vary in their ability to access the germline. In order to determine the extent to which the potential for genetic conflict exists, we characterized the types of genetic relationships between polyps within colonies, using a RAD-Seq approach. Our results indicate that E. larynx colonies are indeed comprised of polyps that are clones and sexually reproduced siblings and offspring, consistent with their life history. In addition, we found that colonies also contain polyps that are genetically unrelated, and that estimates of genome-wide relatedness suggests a potential for conflict within a colony. Taken together, our data suggests that there are distinct categories of relationships in colonies of E. larynx, likely achieved though a range of processes including budding, regeneration and fusion of progeny and unrelated polyps, with the possibility for a genetic conflict resolution mechanism. Together these processes contribute to the re-evolution of the ecologically important trait of coloniality in E. larynx.
Chimere De Strasbourg
Chimère, Sculpture, Cathédrale de Strasbourg, capturée par photofly (ancienne version de 123dCatch, 360Recap) Source: Objaverse 1.0 / Sketchfab
Interspecies chimerism with human embryonic stem cells generates functional human dopamine neurons at low efficiency
<p>Interspecies chimeras offer great potential for regenerative medicine and creation of human disease models. Whether human pluripotent stem cell (hPSC) derived neurons in an interspecies chimera can differentiate into functional neurons and integrate into host neural circuity is not known. Here we show, using Engrailed 1 (En1) as a development niche that human naïve-like ES cells can incorporate into embryonic and adult mouse brains. Human-derived neurons including tyrosine hydroxylase (TH) positive neurons integrate into the mouse brain at low efficiency. These TH-positive neurons have electrophysiologic properties consistent with their human origin. Additionally, these human-derived neurons in the mouse brain accumulate pathologic phosphorylated α-synuclein in response to α-synuclein preformed fibrils. Optimization of human/mouse chimeras could be utilized to study human neuronal differentiation and human brain disorders.</p>
Quantification of the effects of chimerism: datasets
<p>To aid in exploring the effects of chimerism on read mapping, differential expression analysis and <em>de novo</em>assembly, a base set of 26,680 transcripts containing all sequences ranging in length of between 300 and 5000 nt present within the fruit fly cDNA library was created from Ensembl release-100 (https://www.ensembl.org/info/data/ftp/index.html) [1]. These transcripts along with the complete cDNA library from which they were compiled are located within the <strong><em>BaseSetTranscripts</em>.zip</strong> file.</p> <p>This base set of transcripts was used as a reference for simulating reads as required within subsequent sections of our paper (titled: <em>Quantification of the effects of chimerism on read mapping, differential expression and annotation following short-read de novo assembly.</em>). Such simulations often involved hundreds of replicate iterations due to the nature of the study, as well as for the associated creation of modified base sets containing varying portions of chimerism. Parameter values used for read simulations and modified reference sets used within iterations are described in detail within the paper (F1000 <em>paper link to be provided when available.).</em></p> <p>To explore the effects of chimerism on the detection of differentially expressed transcripts ten read datasets, each consisting of five million read-pairs, were simulated using CSReadGen [2] from the base set as described section 2.2 of the manuscript. These are located within the <strong><em>DEReads.zip</em></strong> file. Using these reads differential expression analysis was repeated iteratively, where during each iteration ChimSim [3] was used to create a modified base set to be used as a reference. Within each modified base set created a portion of the transcripts present were made chimeric. The portions of chimerism introduced ranged from 5% to 95% chimeric in steps of five. These modified base sets are located within the d <em><strong>DEChimSimRefs.zip</strong> </em>file. In each case a titles file has also been provided that indicates which transcripts within the base set were made chimeric (if any, e.g. at 0% chimerism this file is empty) and the manner in which chimeras was introduced in accordance to the three types discussed in the paper. For example in the file titled chimeric_refs_0.1_SEQS.fasta 10% of the sequences are chimeric and the file titled chimeric_refs_0.1_TITLES.txt indicates which these are and the type of chimerism introduced.</p> <p>The base set was then used to simulate ten data sets consisting of ten million read-pairs that were each assembled using CStone [4], Trinity [5] and rnaSPAdes [6]. Parameters for read simulations are once again described in detail within our paper (Section 2.3). The assemblies produced by each assembler are contained within the <strong><em>DeNovoAssemblies_SimulatedData.zip</em></strong> file. The two whole body read datasets from Pang et al. [7], following filtering by Trimmomatic [8] as described in our paper, are within the files <em><strong>Reads_RealData_WholeBody_1.zip</strong></em> and <em><strong>Reads_RealData_WholeBody_2.zip</strong></em>, as are the assemblies produced by each of the three assemblers when using these reads as input (<em><strong>DeNovoAssemblies_RealData.zip</strong></em>).</p> <p>Related software to this project are:<br> 1. <a href="http://sourceforge.net/projects/cstone/">CStone</a> <br> 2. <a href="http://sourceforge.net/projects/csreadgen/">CSReadGen</a><br> 3. <a href="https://sourceforge.net/projects/cview/">CView</a><br> 4. <a href="https://sourceforge.net/projects/chimsim/">ChimSim</a> <<br> 5. <a href="https://sourceforge.net/projects/tvscript/">TVScript</a></p> <p>A related poster discussing the the identification of chimerism during assembly is available <a href="https://zenodo.org/record/6022494#.YgY-ji2cbGI">here</a> (DOI: <a href="https://doi.org/10.5281/zenodo.6022493">10.5281/zenodo.6022493</a>) and one discussing the effects of chimerism is available <a href="https://zenodo.org/record/6023171#.YgZmCC2cZQL">here</a> (DOI: <a href="https://doi.org/10.5281/zenodo.6023170">10.5281/zenodo.6023170</a>).</p> <p>General details of the project are available <a href="https://cibio.up.pt/en/projects/de-novo-based-sequence-assembly-of-next-generation-sequence-data-without-chimeras-improved-annotation-gene-expression-profiles-and-haplotype-br-reconstruction/">here</a>.</p> <p> </p> <p><strong>References</strong></p> <p>1. Yates AD, Achuthan P, Akanni W, Allen J, Allen J, Alvarez-Jarreta J, et al. Ensembl 2020. Nucleic Acids Res. 2020;48: D682–D688. doi:10.1093/NAR/GKZ966</p> <p>2. Archer J. CSReadGen website. 2020. Available: https://sourceforge.net/projects/csreadgen/</p> <p>3. Linheiro, Raquel; Archer J. ChimSim website. 2021. Available: https://sourceforge.net/projects/chimsim/</p> <p>4. Linheiro R, Archer J. CStone: A de novo transcriptome assembler for short-read data that identifies non-chimeric contigs based on underlying graph structure. Pertea M, editor. PLOS Comput Biol. 2021;17: e1009631. doi:10.1371/JOURNAL.PCBI.1009631</p> <p>5. Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol 2011 297. 2011;29: 644–652. doi:10.1038/nbt.1883</p> <p>6. Bushmanova E, Antipov D, Lapidus A, Prjibelski AD. rnaSPAdes: a de novo transcriptome assembler and its application to RNA-Seq data. Gigascience. 2019;8: 1–13. doi:10.1093/GIGASCIENCE/GIZ100</p> <p>7. Pang TL, Ding Z, Liang SB, Li L, Zhang B, Zhang Y, et al. Comprehensive Identification and Alternative Splicing of Microexons in Drosophila. Front Genet. 2021;12. doi:10.3389/fgene.2021.642602</p> <p>8. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30: 2114–2120. doi:10.1093/BIOINFORMATICS/BTU170</p>
Simulated data set of chimeric transposable elements.
<p>This dataset is composed of 9,000 sequences of transposable elements (TEs) of 20,000 bp. Three cases of transposable elements with artifacts at the ends, with another chimeric TE or with simple repeats were considered for the generation of the sequences. The sequences of the first case consist of a DNA fragment + first TE + DNA fragment + second TE + DNA fragment. The sequences of the second case consist of a first TE + second TE + repeat of the first TE. The third case sequences consist of a microsatellite that is repeated in tandem by placing the extracted TE at position 10,000 (in the middle), occupying both sides to the ends. All TEs used in this dataset were taken from Dfam, for the species Drosophila melanogaster.</p> <p>The identifier of each sequence has the information about the case, TE Dfam identifier, TE initial position inside the sequence, and the TE length, all separated by "_". For example:</p> <p>Caso1_DF000001548.2_6926_5126</p> <p>File description:</p> <p>dataset.zip: The fasta file containing all the sequences</p> <p>features_data.npy.zip: A numpy file containing the numerical representation of the four TE+Aid plots generated for the sequences presented in the dataset.zip file. This data is actually a numpy array with dimensions 9000x256x256x3x4</p> <p>labels_data.numpy.zip: A numpy file containing the starting and ending position (normalized between 0 and 1) of each TE presented in the dataset.zip file.</p> <p>The last two files were generated to train a neural network for trimming out automatically artifacts in DNA sequences.</p>
Simulation results from WRF-CHIMERE without aerosol feedbacks in eastern China for October-December 2017
<p>Simulation results from WRF-CHIMERE without aerosol feedbacks in eastern China for October-December 2017 including meteorology and air quality:</p><p>YYYYMM_wrfout.zip:</p><p>Meteorological file including wrfout_YYYY-MM-DD_00_00_00 on each day for October-December 2017, which contains these variables:</p><p>T2, Q2, PSFC, U10, V10, RAINC, RAINSH, RAINNC, PBLH, SWDOWN, GLW, SWUPT, LWUPT, CLDFRA, QCLOUD, P, PB, T, VAPOR, PH, PHB</p><p>YYYYMM_out.zip</p><p>Air quality file including out.YYYYMMDD00_24_PHD.nc on each day for October-December 2017, which contains these variables:</p><p>PM25, O3, SO2, NO2, CO and optdaero.</p>
Simulation results from WRF-CHIMERE without aerosol feedbacks in eastern China for July-September 2017
<p>Simulation results from WRF-CHIMERE without aerosol feedbacks in eastern China for July-September 2017 including meteorology and air quality:</p><p>YYYYMM_wrfout.zip:</p><p>Meteorological file including wrfout_YYYY-MM-DD_00_00_00 on each day for July-September 2017, which contains these variables:</p><p>T2, Q2, PSFC, U10, V10, RAINC, RAINSH, RAINNC, PBLH, SWDOWN, GLW, SWUPT, LWUPT, CLDFRA, QCLOUD, P, PB, T, VAPOR, PH, PHB</p><p>YYYYMM_out.zip</p><p>Air quality file including out.YYYYMMDD00_24_PHD.nc on each day for July-September 2017, which contains these variables:</p><p>PM25, O3, SO2, NO2, CO and optdaero.</p>
Simulation results from WRF-CHIMERE without aerosol feedbacks in eastern China for January-March 2017
<p>Simulation results from WRF-CHIMERE without aerosol feedbacks in eastern China for January-March 2017 including meteorology and air quality:</p><p>YYYYMM_wrfout.zip:</p><p>Meteorological file including wrfout_YYYY-MM-DD_00_00_00 on each day for January-March 2017, which contains these variables:</p><p>T2, Q2, PSFC, U10, V10, RAINC, RAINSH, RAINNC, PBLH, SWDOWN, GLW, SWUPT, LWUPT, CLDFRA, QCLOUD, P, PB, T, VAPOR, PH, PHB</p><p>YYYYMM_out.zip</p><p>Air quality file including out.YYYYMMDD00_24_PHD.nc on each day for January-March 2017, which contains these variables:</p><p>PM25, O3, SO2, NO2, CO and optdaero.</p>
Simulation results from WRF-CHIMERE without aerosol feedbacks in eastern China for April-June 2017
<p>Simulation results from WRF-CHIMERE without aerosol feedbacks in eastern China for April-June 2017 including meteorology and air quality:</p><p>YYYYMM_wrfout.zip:</p><p>Meteorological file including wrfout_YYYY-MM-DD_00_00_00 on each day for April-June 2017, which contains these variables:</p><p>T2, Q2, PSFC, U10, V10, RAINC, RAINSH, RAINNC, PBLH, SWDOWN, GLW, SWUPT, LWUPT, CLDFRA, QCLOUD, P, PB, T, VAPOR, PH, PHB</p><p>YYYYMM_out.zip</p><p>Air quality file including out.YYYYMMDD00_24_PHD.nc on each day for April-June 2017, which contains these variables:</p><p>PM25, O3, SO2, NO2, CO and optdaero.</p>
Simulation results from WRF-CHIMERE with enabling aerosol-radiation interactions and aerosol-cloud interactions in eastern China for January-March 2017
<p>Simulation results from WRF-CHIMERE with enabling aerosol-radiation interactions and aerosol-cloud interactions in eastern China for January-March 2017 including meteorology and air quality:</p><p>YYYYMM_wrfout.zip:</p><p>Meteorological file including wrfout_YYYY-MM-DD_00_00_00 on each day for January-March 2017, which contains these variables:</p><p>T2, Q2, PSFC, U10, V10, RAINC, RAINSH, RAINNC, PBLH, SWDOWN, GLW, SWUPT, LWUPT, CLDFRA, QCLOUD, P, PB, T, VAPOR, PH, PHB</p><p>YYYYMM_out.zip</p><p>Air quality file including out.YYYYMMDD00_24_PHD.nc on each day for January-March 2017, which contains these variables:</p><p>PM25, O3, SO2, NO2, CO and optdaero.</p>
Simulation results from WRF-CHIMERE with enabling aerosol-radiation interactions in eastern China for October-December 2017
<p>Simulation results from WRF-CHIMERE with enabling aerosol-radiation interactions in eastern China for October-December 2017 including meteorology and air quality:</p><p>YYYYMM_wrfout.zip:</p><p>Meteorological file including wrfout_YYYY-MM-DD_00_00_00 on each day for October-December 2017, which contains these variables:</p><p>T2, Q2, PSFC, U10, V10, RAINC, RAINSH, RAINNC, PBLH, SWDOWN, GLW, SWUPT, LWUPT, CLDFRA, QCLOUD, P, PB, T, VAPOR, PH, PHB</p><p>YYYYMM_out.zip</p><p>Air quality file including out.YYYYMMDD00_24_PHD.nc on each day for October-December 2017, which contains these variables:</p><p>PM25, O3, SO2, NO2, CO and optdaero.</p>
Simulation results from WRF-CHIMERE with enabling aerosol-radiation interactions in eastern China for July-September 2017
<p>Simulation results from WRF-CHIMERE with enabling aerosol-radiation interactions in eastern China for July-September 2017 including meteorology and air quality:</p><p>YYYYMM_wrfout.zip:</p><p>Meteorological file including wrfout_YYYY-MM-DD_00_00_00 on each day for July-September 2017, which contains these variables:</p><p>T2, Q2, PSFC, U10, V10, RAINC, RAINSH, RAINNC, PBLH, SWDOWN, GLW, SWUPT, LWUPT, CLDFRA, QCLOUD, P, PB, T, VAPOR, PH, PHB</p><p>YYYYMM_out.zip</p><p>Air quality file including out.YYYYMMDD00_24_PHD.nc on each day for July-September 2017, which contains these variables:</p><p>PM25, O3, SO2, NO2, CO and optdaero.</p>
Simulation results from WRF-CHIMERE with enabling aerosol-radiation interactions in eastern China for April-June 2017
<p>Simulation results from WRF-CHIMERE with enabling aerosol-radiation interactions in eastern China for April-June 2017 including meteorology and air quality:</p><p>YYYYMM_wrfout.zip:</p><p>Meteorological file including wrfout_YYYY-MM-DD_00_00_00 on each day for April-June 2017, which contains these variables:</p><p>T2, Q2, PSFC, U10, V10, RAINC, RAINSH, RAINNC, PBLH, SWDOWN, GLW, SWUPT, LWUPT, CLDFRA, QCLOUD, P, PB, T, VAPOR, PH, PHB</p><p>YYYYMM_out.zip</p><p>Air quality file including out.YYYYMMDD00_24_PHD.nc on each day for April-June 2017, which contains these variables:</p><p>PM25, O3, SO2, NO2, CO and optdaero.</p>
Simulation results from WRF-CHIMERE with enabling aerosol-radiation interactions and aerosol-cloud interactions in eastern China for October-December 2017
<p>Simulation results from WRF-CHIMERE with enabling aerosol-radiation interactions and aerosol-cloud interactions in eastern China for October-December 2017 including meteorology and air quality:</p><p>YYYYMM_wrfout.zip:</p><p>Meteorological file including wrfout_YYYY-MM-DD_00_00_00 on each day for October-December 2017, which contains these variables:</p><p>T2, Q2, PSFC, U10, V10, RAINC, RAINSH, RAINNC, PBLH, SWDOWN, GLW, SWUPT, LWUPT, CLDFRA, QCLOUD, P, PB, T, VAPOR, PH, PHB</p><p>YYYYMM_out.zip</p><p>Air quality file including out.YYYYMMDD00_24_PHD.nc on each day for October-December 2017, which contains these variables:</p><p>PM25, O3, SO2, NO2, CO and optdaero.</p>
Simulation results from WRF-CHIMERE with enabling aerosol-radiation interactions and aerosol-cloud interactions in eastern China for July-September 2017
<p>Simulation results from WRF-CHIMERE with enabling aerosol-radiation interactions and aerosol-cloud interactions in eastern China for July-September 2017 including meteorology and air quality:</p><p>YYYYMM_wrfout.zip:</p><p>Meteorological file including wrfout_YYYY-MM-DD_00_00_00 on each day for July-September 2017, which contains these variables:</p><p>T2, Q2, PSFC, U10, V10, RAINC, RAINSH, RAINNC, PBLH, SWDOWN, GLW, SWUPT, LWUPT, CLDFRA, QCLOUD, P, PB, T, VAPOR, PH, PHB</p><p>YYYYMM_out.zip</p><p>Air quality file including out.YYYYMMDD00_24_PHD.nc on each day for July-September 2017, which contains these variables:</p><p>PM25, O3, SO2, NO2, CO and optdaero.</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.