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3,696 results for “reference”
Fig. 1. Paulo E in The Herpetological Collection Of Maximilian, Prince Of Wied (1782-1867), With Special Reference To Brazilian Materials
Fig. 1. Paulo E. Vanzolini. On Serra Tapeque´m (a tepui remnant), Roraima, NE Brazil (July 14,
Fig. 29 in The Herpetological Collection Of Maximilian, Prince Of Wied (1782-1867), With Special Reference To Brazilian Materials
Fig. 29. Surviving syntype of Rana sibilatrix Wied, 1824 (AMNH A-485). 5 mm scale line.
Fig. 28 in The Herpetological Collection Of Maximilian, Prince Of Wied (1782-1867), With Special Reference To Brazilian Materials
Fig. 28. Holotype of Hyla elegans Wied, 1824 (AMNH A-784).
Fig. 15 in The Herpetological Collection Of Maximilian, Prince Of Wied (1782-1867), With Special Reference To Brazilian Materials
Fig. 15. Holotype of Coluber plumbeus Wied (AMNH R-333653). Scale line 5 10 mm.
Mappability .bigwig reference files for hg38/hg19; various read lengths
<p>This dataset contains reference mappability .bigwig files for the reference genomes hg38 and hg19, for various read lengths. The files were generated with the GEM library (https://sourceforge.net/projects/gemlibrary/files/gem-library/Binary%20pre-release%203/GEM-binaries-Linux-x86_64-core_i3-20130406-045632.tbz2) using <a href="https://raw.githubusercontent.com/epigen/LIQUORICE/master/liquorice/create_mappability_bigwigs.sh">this</a> script (based on <a href="https://evodify.com/gem-mappability/">this</a> work).</p> <p>The files can be used as input for LIQUORICE (<a href="https://liquorice.readthedocs.io">https://liquorice.readthedocs.io</a>) - just select the file that matches your sequencing protocol (i.e. read length) and the reference genome of your mapped .bam files.</p>
IPCC-AR6-SPM material + info on reference regions
<p>### Contents</p> <p>Included material is from sources indicated below.</p> <p>- `spm/` from **[1]**<br> - `reference-regions/` from **[2]** except for `hexagon_grid_locations.csv`<br> - `reference-regions/hexagon_grid_locations.csv` from **[3]**</p> <p>### [1] source of the data in `spm/`</p> <p>```<br> IPCC, 2021: Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S. L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T. K. Maycock, T. Waterfield, O. Yelekçi, R. Yu and B. Zhou (eds.)]. Cambridge University Press. In Press.<br> ```</p> <p>```<br> Gillett, N.P.; Malinina, E.; Kaufman, D.; Neukom, R. (2021): Summary for Policymakers of the Working Group I Contribution to the IPCC Sixth Assessment Report - data for Figure SPM.1 (v20210809). NERC EDS Centre for Environmental Data Analysis, 09 August 2021. doi:10.5285/76cad0b4f6f141ada1c44a4ce9e7d4bd. http://dx.doi.org/10.5285/76cad0b4f6f141ada1c44a4ce9e7d4bd<br> ```</p> <p>### [2] source of the data in csv files</p> <p>```<br> Iturbide, Maialen, Fernández, Jesús, Gutiérrez, José Manuel, Bedia, Joaquín, Cimadevilla, Ezequiel, Díez-Sierra, Javier, Manzanas, Rodrigo, Casanueva, Ana, Baño-Medina, Jorge, Milovac, Josipa, Herrera, Sixto, Cofiño, Antonio S., San Martín, Daniel, García-Díez, Markel, Hauser, Mathias, Huard, David, & Yelekci, Özge. (2021). Repository supporting the implementation of FAIR principles in the IPCC-WGI Atlas (v2.0-final). Zenodo. https://doi.org/10.5281/zenodo.5171760<br> ```</p> <p>### [3] source of the data in csv files</p> <p>```<br> Gael Forget, Lauren Milechin, & Philippe Roy. (2021). JuliaClimate/GlobalOceanNotebooks: new webpage, docker+sysimage+Pluto, add IPCC+other notebooks, cleanup (v0.3.5). Zenodo. https://doi.org/10.5281/zenodo.5537709<br> ```</p>
Dataset from: A curated DNA barcode reference library for parasitoids of northern European cyclically outbreaking geometrid moths
<p><span>Large areas of forests are annually damaged or destroyed by outbreaking insect pests. Understanding the factors that trigger and terminate such population eruptions has become crucially important, as plants, plant-feeding insects, and their natural enemies may respond differentially to the ongoing changes in the global climate. In northernmost Europe, climate-driven range expansions of the geometrid moths <em>Epirrita autumnata</em> and <em>Operophtera brumata</em> have resulted in overlapping and increasingly severe outbreaks. Delayed density-dependent responses of parasitoids are a plausible explanation for the ten-year population cycles of these moth species, but the impact of parasitoids on geometrid outbreak dynamics is unclear due to a lack of knowledge on the host ranges and prevalences of parasitoids attacking the moths in nature. To overcome these problems, we reviewed the literature on parasitism in the focal geometrid species in their outbreak range, and then constructed a DNA barcode reference library for all relevant parasitoid species based on reared specimens and sequences obtained from public databases. The combined parasitoid community of<em> E. autumnata</em> and <em>O. brumata</em> consists of 32 hymenopteran species, all of which can be reliably identified based on their barcode sequences. The curated barcode library presented here opens up new opportunities for estimating the abundance and community composition of parasitoids across populations and ecosystems based on mass barcoding and metabarcoding approaches. Such information can be used for elucidating the role of parasitoids in moth population control, possibly also for devising methods for reducing the extent, intensity, and duration of outbreaks.</span></p>
GRCh38.p13 Reference FASTA (bgzip'd with faidx)
<p>This is derived from https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/001/405/GCF_000001405.39_GRCh38.p13/GRCh38_major_release_seqs_for_alignment_pipelines/GCA_000001405.15_GRCh38_full_plus_hs38d1_analysis_set.fna.gz.</p> <p>All non-primary sequences have been removed.</p> <p>It has then been recompressed with bgzip and indexed with samtools:</p> <pre><code class="language-bash">curl -#fSL https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/001/405/GCF_000001405.39_GRCh38.p13/GRCh38_major_release_seqs_for_alignment_pipelines/GCA_000001405.15_GRCh38_full_plus_hs38d1_analysis_set.fna.gz -o genomic.fna.gz gunzip genomic.fna.gz awk '{ if ((NR>1)&&($0~/^>/)) { printf("\n%s", $0); } else if (NR==1) { printf("%s", $0); } else { printf("\t%s", $0); } }' genomic.fna | grep -v "^>chr\S*_" - | tr "\t" "\n" > genomic.short.fna bgzip -c genomic.short.fna > reference.fna.bgz samtools faidx reference.fna.bgz tar -czvf GRCh38_reference_fasta.tar reference.fna.bgz reference.fna.bgz.fai reference.fna.bgz.gzi </code></pre> <p> </p> <p>This tar file contains:</p> <ul> <li>reference.fna.bgz</li> <li>reference.fna.bgz.fai</li> <li>reference.fna.bgz.gzi</li> </ul> <p> </p>
Galaxy tutorial for reference-based RNA-seq analysis
<p>https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/ref-based/tutorial.html#functional-enrichment-analysis-of-the-de-genes</p> <p>the `compute` tool in the tutorial doesn't work. I used R to process the dataset and provide it so the students can continue with the tutorial.</p>
NTUH Reference strain
<p><a href="https://www.ncbi.nlm.nih.gov/data-hub/taxonomy/484021"><em>Klebsiella pneumoniae</em> subsp. <em>pneumoniae</em> NTUH-K2044</a>, reference strain used in ALPPACA</p>
Functional annotation of the reference transcriptome of Mesodinium rubrum strain JAMR
<p>Raw reads were pre-processed by removing the adaptors and low-quality reads using BBMap. The filtered reads were normalized for depth based on kmer counts using BBNorm function. De novo transcriptomes were generated using both Trinity and velvet-oases. CD-HIT-EST was used to merge the two de novo transcriptomes and reduce the transcript redundancy to 98% similarity and generate unique genes. Transcriptome assembly completeness was evaluated with BUSCO (Benchmarking Universal Single Copy Orthologs) database. Functional annotation was done using blastp function of ncbi-blast using the nr database with evalue 1E-20 and num_alignments 3.</p>
Functional annotation of the reference transcriptome of Dinophysis acuminata strain DAVA01
<p>Raw reads were pre-processed by removing the adaptors and low-quality reads using BBMap. The filtered reads were normalized for depth based on kmer counts using BBNorm function. De novo transcriptomes were generated using both Trinity and velvet-oases. CD-HIT-EST was used to merge the two de novo transcriptomes and reduce the transcript redundancy to 98% similarity and generate unique genes. Transcriptome assembly completeness was evaluated with BUSCO (Benchmarking Universal Single Copy Orthologs) database. Functional annotation was done using blastp function of ncbi-blast using the nr database with evalue 1E-20 and num_alignments 3.</p>
Functional annotation of the reference transcriptome of Mesodinium rubrum strain MBL-DK2009
<p>Raw reads were pre-processed by removing the adaptors and low-quality reads using BBMap. The filtered reads were normalized for depth based on kmer counts using BBNorm function. De novo transcriptomes were generated using both Trinity and velvet-oases. CD-HIT-EST was used to merge the two de novo transcriptomes and reduce the transcript redundancy to 98% similarity and generate unique genes. Transcriptome assembly completeness was evaluated with BUSCO (Benchmarking Universal Single Copy Orthologs) database. Functional annotation was done using blastp function of ncbi-blast using the nr database with evalue 1E-20 and num_alignments 3.</p>
Functional annotation of the reference transcriptome of Teleaulax amphioxeia
<p>Raw reads were pre-processed by removing the adaptors and low-quality reads using BBMap. The filtered reads were normalized for depth based on kmer counts using BBNorm function. De novo transcriptomes were generated using both Trinity and velvet-oases. CD-HIT-EST was used to merge the two de novo transcriptomes and reduce the transcript redundancy to 98% similarity and generate unique genes. Transcriptome assembly completeness was evaluated with BUSCO (Benchmarking Universal Single Copy Orthologs) database. Functional annotation was done using blastp function of ncbi-blast using the nr database with evalue 1E-20 and num_alignments 3.</p>
Functional annotation of the reference transcriptome of Dinophysis ovum strain DoSS3195
<p>Raw reads were pre-processed by removing the adaptors and low-quality reads using BBMap. The filtered reads were normalized for depth based on kmer counts using BBNorm function. De novo transcriptomes were generated using both Trinity and velvet-oases. CD-HIT-EST was used to merge the two de novo transcriptomes and reduce the transcript redundancy to 98% similarity and generate unique genes. Transcriptome assembly completeness was evaluated with BUSCO (Benchmarking Universal Single Copy Orthologs) database. Functional annotation was done using blastp function of ncbi-blast using the nr database with evalue 1E-20 and num_alignments 3.</p>
Gene Ontology Data Reference
<p>Referential manifest of GO data releases.</p>
DGFI-TUM DSO1 orbits of altimetry satellites TOPEX/Poseidon, Jason-1, Jason-2 and Jason-3 derived from SLR data in the SLRF2014 reference frame
<p>The data set provides DSO1 orbits of altimetry satellites TOPEX/Poseidon (27 September 1992 to 9 October 2005), Jason-1 (13 January 2002 to 30 June 2013), Jason-2 (20 July 2008 to 2 October 2019) and Jason-3 (17 February 2016 to 24 October 2021) computed at the Deutsches Geodätisches Forschungsinstitut of the Technical University of Munich (DGFI-TUM). The orbits are derived using the DGFI-TUM Orbit and Geodetic parameter estimation Software (DOGS). The orbits were computed from SLR data in the SLRF2014 (an extended version of ITRF2014) reference frame using common for all satellites, most precise models and standards available and described in the following paper that serves as a citation of these orbits:</p> <p>Sergei Rudenko, Denise Dettmering, Julian Zeitlhöfler, Riva Alkahal, Dhruv Upadhyay and Mathis Bloßfeld (2023) Radial orbit errors of contemporary altimetry satellite orbits. Surveys in Geophysics, https://doi.org/10.1007/s10712-022-09758-5.</p> <p>For each satellite, a tar file is given comprising compressed files. File names are given as satgpswd.sp3.gz, where “sat” is the abbreviation of the satellite name (JA1, JA2, JA3, TPX), “gpsw” is the 4-digit GPS week, and “d” indicates the day of the GPS week containing the first time instant of the file (0 = Sunday, 6 = Saturday). The orbit files are available in the Extended Standard Product 3 Orbit Format, Version c (SP3-c).</p> <p>The orbits were derived within the project “Mitigation of the current errors in precise orbit determination of altimetry satellites (MEPODAS)” funded by Deutsche Forschungsgemeinschaft (DFG).</p>
IDSL.CSA spectra annotation table for ST001000 CSA spectra annotated by ST000923 CSA library (reference)
<p>IDSL.CSA spectra annotation table for ST001000 CSA spectra annotated by ST000923 CSA library (reference)</p>
2nd version of modified GRCh38 reference
<p>This is the 2nd version of Modified GRCh38 reference that excludes decoys related to FANCD2, DUSP22 and GPRIN2 genes as these are the complex genes.</p>
Production of microplastic reference materials
<p>The validation of analytical methods for the identification and quantification of micro and nanoplastics in environmental matrices is hindered by a lack of standard reference materials (RMs). These materials are required for the harmonisation of analytical methods and the generation of comparable and reliable data. As a result, uncertainties associated with the measurements from individual laboratories can occur due to contamination, over-estimation, and under-estimation of nano and microplastics from environmental samples affecting the comparison and interpretation of analytical data from different laboratories. Until now, the Norwegian Institute for Water Research (NIVA) has focused on making reference materials (RMs) in the microscale, i.e., from 50 μm and up to 1 mm of the following polymer types: PVC, PE, PET, PS, PP and PC as well as polymer mixtures, in the form of effervescent soda tablets. These RMs have been used in inter- laboratory comparison (ILC) studies worldwide as well as in microplastic recovery tests. The RMs have been analysed by different laboratories using different techniques, including light microscopy, μFTIR, Raman and Pyr-GC/MS. The latest ILC study including the soda tablets is the EU project EUROqCHARM, where two sets of soda tablets, one containing PE, PET and PS (50-300 μm) and the other containing PP, PC and PVC (50-300 μm) were produced. A complete characterisation of these batches was carried during production by counting the number of particles in 20 tablets obtaining a RSD of 11 % and 13 %, respectively and by measuring all the particles in 10 of the tablets for each batch to study the size distribution. In this presentation the results for the complete characterisation of the tablets will be shown. The next step in generating relevant RMs is to focus attention on the size fractions below Also see: https://micro2022.sciencesconf.org/425663/document</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.