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3,696 results for “Reference”
Pre-built leave-out-out cross-validation imputation reference panel datasets - LmTag paper
<p>Pre-built leave-out-out cross-validation imputation reference panel datasets - LmTag paper (complement for a tutorial at https://github.com/datngu/LmTag)</p> <p>This repo includes chromosome 10 reference panel data constructed for 3 populations:</p> <p>- EAS</p> <p>- EUR</p> <p>- SAS</p>
DNA sequence and taxonomic gap analyses to quantify the coverage of aquatic cyanobacteria and eukaryotic microalgae in reference databases: Results of a survey in the Alpine region
<p>This dataset has been prepared as part of the Interreg Alpine Space project Eco-AlpsWater (ASP569) - <em>Innovative Ecological Assessment and Water Management Strategy for the Protection of Ecosystem Services in Alpine Lakes and Rivers</em>, <a href="https://www.alpine-space.eu/projects/eco-alpswater/en/home">https://www.alpine-space.eu/projects/eco-alpswater/en/home</a></p> <p>Individual archives include 16S rRNA (cyanobacteria) and 18S rRNA (microalgae) FASTA sequences and associated blastn results obtained from the high throughput sequencing of plankton and biofilm bulk/eDNA samples collected in 2019 in 37 lakes and 22 rivers across the Alpine region. These are supporting files for the paper by Salmaso et al., 2022. DNA sequence and taxonomic gap analyses to quantify the coverage of aquatic cyanobacteria and eukaryotic microalgae in reference databases: Results of a survey in the Alpine region. Science of the Total Environment, in press.</p>
Standard Reference Database : ITV-CORE
<p>Decisions demand data, and poor quality data can lead to wrong, inaccurate, or late decisions. The way in which data is collected, stored and shared will reverberate in its quality and accuracy, consequently, reflecting on the ability to understand the aspects they represent. The private sector acting in the environmental area demands objectivity and assertiveness, and that is why it is essential to treat the data that subsidize conservation and restoration actions with exceptional care. To assess the state of biodiversity and environmental impacts, extensive field surveys are often required; for this, independent service providers are hired, who are specialized in obtaining a variety of types of information. Consequently, different collection methods are applied, and almost always methodological and formatting inconsistencies can be found in the resulting data. For the subsequent integration of this data into databases, it will be necessary to extract, adjust and standardize them, generating an entirely new demand, consuming time, human effort and financial resources. In addition, this demand also increases the risk of misinterpretation, typing and digitization errors, which can compromise quality, or even lead to loss of information. The standardization of data used in the survey, inventories, storage and sharing processes is a strategic solution to increase efficiency, reduce costs and risks of information degradation and loss. Furthermore, it brings a number of other benefits, such as the transformation of the analogic field recording system (field notebooks) to an entirely digital format, with the integration of cameras, tablets, dataloggers, and other widely available technologies. When it comes to preparing a recommendation for the standardization of data in a comprehensive and inclusive way, we mapped the biodiversity data frequently used by researchers from the Biodiversity and Ecosystem Services group at The Instituto Tecnológico Vale. Through this mapping, we seek to understand the types of data that already exist, how they have been used, stored and shared in databases, but also their convergence and peculiarities. With the participation of researchers, we seek to develop and validate a preliminary system of terms and metadata, including recommendations for best practices, aiming to improve the use of environmental and biodiversity data. The mapping showed a series of correspondences regarding the types of data used by the BES-ITV group, especially in the data applied in studies of Conservation and Restoration, Landscape Ecology, Genomics and Radio Frequency Identification. But also a great diversity of research topics (Total=29), focusing on six large biological groups, aspects that demonstrate the high multidisciplinary and wide coverage of environmental, ecological, genetic and biodiversity data used by the group. Based on these results, a system of terms and metadata is being developed, as well as the idealization of a modular system for the automatic generation of field digital spreadsheets, in order to simplify data collection through exclusively digital means.</p> <p> </p>
Dataset: A systematic multi-technique comparison of luminescence characteristics of two reference quartz samples
<p>Original dataset corresponding to the manuscript Schmidt et al. submitted to the Journal of Luminescence. The dataset is structured as follows: </p> <ul> <li>01_original_data <ul> <li>This folder contains the original measurement data for each experiment (e.g., LM-OSL, TL), organised following the manuscript figures, e.g., Fig_4. These subfolders contain the original measurement data and the measurement sequence. For Risø readers, the output format is *.binx and *.xsyg for results from the Freiberg Instruments lexsyg readers (e.g., TR-OSL).</li> </ul> </li> <li>02_processed_data <ul> <li>Comma-separated value (*.csv) to reproduce each figure in the manuscript. </li> </ul> </li> </ul> <p> </p>
Raw and aggregated data for the study introduced in the paper "The way we cite: common metadata used across disciplines for defining bibliographic references"
<p>These data have been gathered in the context of a study aiming to investigate citation practices for referencing different types of entities and, in particular, for understanding the most used metadata in bibliographic references. The data are stored in two documents in XLSX format:</p> <ul> <li>file "links-intext-pointers-and-cited-entity-types.xlsx" - it contains information about whether the in-text reference pointers of the various PDF articles of the corpus have specified hypertextual links from the in-text reference pointers to the denoted bibliographic reference, plus information about the types of all the entities cited by each article in the corpus;</li> <li>file "metadata-bibliographic-references.xlsm" - it contains information about the metadata used to identify the various descriptive elements of all the bibliographic references defined in the article of the corpus.</li> </ul> <p>The methodology used to gather all these data is described in:</p> <blockquote> <p>Santos, E. A. d., Peroni, S., Mucheroni, M. L.: Workflow for retrieving all the data of the analysis introduced in the article "Citing and referencing habits in Medicine and Social Sciences journals in 2019". (2020), <a href="https://doi.org/10.17504/protocols.io.bbifikbn">https://doi.org/10.17504/protocols.io.bbifikbn</a></p> </blockquote>
TRESCA D1.2 Desk Research Documents (Reference List)
<p>Documents selected for further analysis within the secondary research process of Deliverable 1.2 (Science Communication and Policy Trend Report) of the H2020 project TRESCA "Trustworthy, Reliable and Engaging Scientific Communication Approaches".</p>
Fiware4Water Reference Architecture
<p>Fiware4Water Reference Architecture developed in the Fiware4Water EU Project founded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 821036.</p>
Zonal mean data of the SOCOLv4 reference experiment (1980-2018)
<p>Zonal mean data of the SOCOLv4 reference experiment (1980-2018). O3, NOx, ClOx, HOx, BrOx, Temperature.</p>
EukRibo: a manually curated eukaryotic 18S rDNA reference database
<p>EukRibo is a manually curated database of reference small-subunit ribosomal RNA gene (18S rDNA) sequences of eukaryotes, specifically aimed at taxonomic annotation of high-throughput metabarcoding datasets. Unlike other reference databases of ribosomal genes, it is not meant to exhaustively capture all publicly available 18S rDNA sequences from the INSDC repositories, but to represent a subset of highly trustable sequences covering the whole known diversity of eukaryotes, with a focus on protists, manually verified taxonomic identifications, and relatively low genetic redundancy.</p> <p>EukRibo is part of a suite of public resources generated by the UniEuk project (www.unieuk.org), which are all designed to follow a common taxonomic framework for maximal interoperability. The high level of taxonomic accuracy of EukRibo, together with a newly designed, phylogenetically-informed annotation approach, allow high confidence in the taxonomic annotation of environmental metabarcodes, as well as identification of new eukaryotic diversity at various taxonomic levels using a connected components approach.</p> <p>* * *</p> <p>Accompanying preprint available at <a href="https://doi.org/10.1101/2022.11.03.515105">https://doi.org/10.1101/2022.11.03.515105</a>.</p> <p>* * *</p> <p><strong>EukRibo ReadMe file, versions 1 and 2</strong></p> <p>Each EukRibo release consists of <strong>4 files</strong>:<br> - a <strong>tsv table </strong>containing the taxonomic and other information about the 18S rDNA sequences included in the release<br> - a <strong>fasta file </strong>containing the <strong>full sequences </strong>as retrieved from the INSDC repositories (NCBI, EMBL-EBI/ENA, DDBJ)<br> - a <strong>fasta file </strong>containing the <strong>variable region V4 </strong>extracted from all these sequences (based on the fragment amplified with the Tara-Oceans V4 primers)<br> - a <strong>fasta file </strong>containing the <strong>variable region V9 </strong>extracted from the subset of sequences where it is present (based on the fragment amplified with the Tara-Oceans V9 primers)</p> <p>The primary goal of EukRibo was to be used to annotate the EukBank meta-dataset of available V4 metabarcoding datasets, and therefore all sequences included in EukRibo contain the variable region V4.<br> Only a subset of these sequences (about 75%) also contain the variable region V9; this is because many 18S rDNA sequences in the INSDC repositories stop before the V9 fragment.</p> <p>Sequences with slightly incomplete V4 or V9 fragments were kept if phylogenetically useful - i.e. if they are the only available representatives of a certain taxonomic lineage.<br> <strong>V4 </strong>We allowed up to 50 missing positions in the relatively conserved area at the 5' end of the V4 fragment (for an average fragment length of about 380 bp); no sequence incomplete at the 3' end of the V4 fragment is included.<br> <strong>V9 </strong>We allowed up to 30 missing positions in the relatively conserved area at the 3' end of the V9 fragment (for an average length of about 135 bp); no sequence incomplete at the 5' end of the V9 fragment is included.<br> We allowed a higher proportion of missing positions for the V9 region because being more conservative would imply losing too many sequences, including entire taxonomic lineages.</p> <p><strong>Version 1 of EukRibo</strong><br> This is the starting version of EukRibo that was used for the taxonomic annotation of the EukBank dataset, with taxonomy strings that were fixed as of October 2020.<br> - Contains 46,345 sequences with a sufficiently complete V4 region; 46,299 with the actual complete V4 region and 46 (about 0.1%) with missing positions at the 5' end.<br> - Of these, 34,438 also include a sufficiently complete V9 region; 23,226 with the actual complete V9 region and 11,206 (about 33%) with missing positions at the 3' end.</p> <p><strong>Version 2 of EukRibo</strong><br> This is a version of EukRibo that was made taxonomically compatible with version 3 of the EukProt database (<a href="https://doi.org/10.1101/2020.06.30.180687">https://doi.org/10.1101/2020.06.30.180687</a>), with taxonomic revisions as of July 2022 as well as additional information on the included selection of sequences that was not provided in the tsv file of version 1.<br> - Contains the exact same selection of sequences as in version 1, with the addition of genus <em>Meteora</em>, the last remaining known supergroup-level eukaryotic lineage for which an 18S rDNA was not previously available. (The <em>Meteora </em>sequence contains the full V4 fragment but does not include a sufficiently complete V9 fragment.)<br> - Only 34,432 sequences with a sufficiently complete V9 region are now retained because of 6 previously unrecognised chimeric sequences where the V9 fragment does not originate from the same organism as the V4 fragment.</p> <p><strong>Files in EukRibo version 1</strong>:<br> 46345_EukRibo.tsv.gz<br> 46345_EukRibo_full_seqs.fas.gz<br> 46345_EukRibo_V4.fas.gz<br> 34438_EukRibo_V9.fas.gz</p> <p>The tsv file contains 6 columns:<br> <strong>gb_accession </strong>- INSDC accession number of the sequence<br> <strong>supergroup</strong>, <strong>taxogroup1</strong>, <strong>taxogroup2 </strong>- binning of the taxa into strictly monophyletic clades of evolutionary and/or ecological significance<br> <strong>UniEuk_taxonomy_string </strong>- full UniEuk-compatible taxonomic annotation of the sequence<br> - an unlimited number of levels is allowed (going down to strain for isolated organisms or to clone for environmental sequences)<br> - informal names are used for phylogenetically supported clades without formal name<br> <strong>V9 </strong>- presence ('Y') or absence ('N') of a sufficiently complete V9 fragment in the sequence</p> <p><strong>Files in EukRibo version 2</strong>:<br> 46346_EukRibo-02.tsv.gz<br> 46346_EukRibo-02_full_seqs.fas.gz<br> 46346_EukRibo-02_V4.fas.gz<br> 34432_EukRibo-02_V9.fas.gz</p> <p>The tsv file now contains 12 columns:<br> <strong>gb_accession</strong>, <strong>supergroup</strong>, <strong>taxogroup1</strong>, <strong>taxogroup2</strong>, <strong>UniEuk_taxonomy_string</strong><br> - same columns as in version 1<br> <strong>alternative_strain_names </strong>(new) - provides alternative strain/isolate names when known to help cross-linking genetic data coming from the same organism<br> <strong>V4 </strong>(new) - indicates whether the V4 fragment is complete ('yes - complete') or missing positions at the 5' end ('yes - partial')<br> <strong>V9 </strong>(emended content) - now contains more precise information than in version 1 about whether it is complete ('yes - complete'), missing positions at the 3' end ('yes - partial'), or was excluded, and the 6 possible reasons why ('no - missing', 'no - too incomplete', 'no - chimera', 'no - bad quality', 'no - deletion in V9', 'no - Ns in V9')<br> <strong>EukProt_ID_same_strain </strong>(new) - accession of EukProt datasets from the same isolate<br> <strong>EukProt_ID_different_strain </strong>(new) - accession of EukProt datasets from a different isolate of the same species<br> <strong>columns_modified_since_previous_version </strong>(new) - lists all of the 6 pre-existing columns that have a modified content compared to version 1<br> <strong>remarks </strong>(new) - additional information such as presence of an intron in the V9 fragment, taxonomic identity of the two parts of chimeric sequences, or the presence of Ns or a deletion in the V4 or the V9 fragment (but insufficient to warrant exclusion)</p>
A termite genome reference and its Bowtie2 index
<p>This dataset contains a fasta file and its Bowtie2 index. The fasta file includes publicly available genomes of 5 termite species, namely <em>Zootermopsis nevadensis </em>(Terrapon, N., Li, C., Robertson, H. M., Ji, L., Meng, X., Booth, W., ... & Liebig, J. (2014). Molecular traces of alternative social organization in a termite genome. Nature communications, 5(1), 1-12.), <em>Cryptotermes secundus</em> (Harrison, M. C., Jongepier, E., Robertson, H. M., Arning, N., Bitard-Feildel, T., Chao, H., ... & Bornberg-Bauer, E. (2018). Hemimetabolous genomes reveal molecular basis of termite eusociality. Nature ecology & evolution, 2(3), 557-566.), <em>Macrotermes natalensis</em> (Poulsen, M., Hu, H., Li, C., Chen, Z., Xu, L., Otani, S., ... & Zhang, G. (2014). Complementary symbiont contributions to plant decomposition in a fungus-farming termite. Proceedings of the National Academy of Sciences, 111(40), 14500-14505.), <em>Coptotermes formosanus</em> (Draft genome sequence of the termite, Coptotermes formosanus: Genetic insights into the pyruvate dehydrogenase complex of the termite) and <em>Reticulitermes speratus </em>(Shigenobu, S., Hayashi, Y., Watanabe, D., Tokuda, G., Hojo, M. Y., Toga, K., Saiki, R., Yaguchi, H., Masuoka, Y., Suzuki, R., Suzuki, S., Kimura, M., Matsunami, M., Sugime, Y., Oguchi, K., Niimi, T., Gotoh, H., Hojo, M. K., Miyazaki, S., … Maekawa, K. (2022). Genomic and transcriptomic analyses of the subterranean termite Reticulitermes speratus: Gene duplication facilitates social evolution. Proceedings of the National Academy of Sciences, 119(3), e2110361119.). These genomic sequences have been classified with Kraken 2 v2.1.2 (Wood, D. E., Lu, J., & Langmead, B. (2019). Improved metagenomic analysis with Kraken 2. Genome Biology, 20(1), 1–13 and Wood, D. E., & Salzberg, S. L. (2014). Kraken: Ultrafast metagenomic sequence classification using exact alignments. Genome Biology, 15(3).) to remove all microbial sequences. This cleaned fasta file was indexed using the bowtie2-build command from Bowtie2 (Langmead, B., & Salzberg, S. L. (2012). Fast gapped-read alignment with Bowtie 2. Nature Methods, 9(4), 357–359.) and can be used to perform alignments.</p>
References and Metadata for Electric vehicles' consumer behaviours: Mapping the field and providing a research agenda (https://doi.org/10.1016/j.jbusres.2022.06.011)
<p>The bibliography and metadata used for the analysis published in the Journal of Business Research - Electric vehicles' consumer behaviors: Mapping the field and providing a research agenda (https://doi.org/10.1016/j.jbusres.2022.06.011).</p>
Four Reference Quality Genome Assemblies of Pyrenophora teres f. maculata: A Resource for Studying the Barley Spot Form Net Blotch Interaction
<p>Updated draft genome assembly (FASTA) and annotation (GFF) for the <em>P. teres </em>f.<em> maculata </em>isolate FGOB10Ptm-1. </p>
Four Reference Quality Genome Assemblies of Pyrenophora teres f. maculata: A Resource for Studying the Barley Spot Form Net Blotch Interaction
<p>Updated draft genome assembly (FASTA) and annotation (GFF) for the <em>P. teres </em>f.<em> maculata </em>isolate P-A14. </p>
Data used in ECLIPSER methods paper and GTEx snRNA-seq cross-tissue reference map analysis
<p>The tables were used in the papers: Rouhana*, Wang* <em>et al.,</em> ECLIPSER: identifying causal cell types and genes for complex traits through single cell enrichment of e/sQTL-mapped genes in GWAS loci, bioRxiv 2021, doi: https://doi.org/10.1101/2021.11.24.469720; and Eraslan <em>et al.,</em> Single-nucleus cross-tissue molecular reference maps to decipher disease gene function, bioRxiv 2021, doi: https://doi.org/10.1101/2021.07.19.452954. '<a href="https://zenodo.org/api/files/1f8d48d0-6bf7-4bec-b6ef-5c7a9ead8079/GTEx_v8_HG38_all_variants.tsv.gz">GTEx_v8_HG38_all_variants.tsv.gz</a>' is an input file for running GWASvar2gene on GTEx v8 eQTLs and sQTLs, and all other files are input files for ECLIPSER.</p>
MALDI-TOF-MS reference spectra and sequence data for domesticated equids (horse and donkey) collagen for Zooarchaeology by Mass Spectrometry (ZooMS)
<p>MALDI-TOF-MS spectra of extracted collagen from modern reference and archaeological bone samples to develop markers for Zooarchaeology by Mass Spectrometry (ZooMS) to distinguish between Equus species. For each sample digestions were done in both trypsin and chymotrypsin separately. Information about the species of the samples can be found in 'sample metadata.csv' file. Information on the extraction and digestion protocol can be found in the associated manuscript. The sequence data contains alignments of the proteins COL1A1 and COL1A2 for available Equus collagen protein sequences. More information on these files can be found in the corresponding manuscript to this dataset.<br> </p>
Control panel created from 30 Nanopore sequencing data from the Human Pangenome Reference Consortium
<p>This is control panel for <a href="https://github.com/friend1ws/nanomonsv">nanomonsv</a> software, which is expected to exclude many false positives as well as improve computational cost. This is made by aligning 30 Nanopore sequencing data from Human Pangenome Reference Consortium to the GRCh38 reference genome (obtained from <a href="https://console.cloud.google.com/storage/browser/genomics-public-data/resources/broad/hg38/v0;tab=objects">here</a>) with <a href="https://github.com/lh3/minimap2">minimap2</a> version 2.24. <strong>When you use these control panels and publish, do not forget to credit to <a href="https://humanpangenome.org/data-use-protocol/">HPRC</a>!</strong></p>
Comprehensive 100-bp resolution genome-wide epigenomic profiling data for the hg38 human reference genome
<p>This is a comprehensive collection of diverse epigenomic profiling data in 100-bp resolution with full genome-wide coverage. The datasets are processed from raw read count data collected from five types of sequencing-based assays collected by the Encyclopedia of DNA Elements (ENCODE, <a href="http://www.encodeproject.org">http://www.encodeproject.org</a>) consortium. A total of 6,305 alignment profiles from various high-throughput sequencing assays available on the ENCODE database were preprocessed and filtered according to ENCODE’s data standard</p>
Baseline map of 137Cs inventories in reference soil sites at the continental scales of South America
<p>This dataset contains the baseline map of <sup>137</sup>Cs inventories in reference soil sites (Bq m<sup>-2</sup>, decay-corrected to 2020) estimated by Partial Least Square Regression (PLSR) with a spatial resolution of 2 km at the continental scale of South America, as well as the prediction uncertainties of the baseline map (coefficient of variation, %).<br> Details information regarding this dataset can be found in the original publication:<br> Mapping the spatial distribution of global <sup>137</sup>Cs fallout in soils of South America as a baseline for Earth Science studies, Earth-Science Reviews, Volume 214, 2021, 103542, ISSN 0012-8252, https://doi.org/10.1016/j.earscirev.2021.103542.</p>
Human kidney cortex CODEX reference dataset 1
<p>CODEX image stack of human kidney cortex stained with markers as indicated in CODEX_antibody_list_010621.csv and imaged in the order given in CODEX_channel_index_010621.csv. Tissue preparation and analysis as described <a href="https://www.biorxiv.org/content/10.1101/2021.12.27.474025v1">here.</a></p>
Diagnostic accuracy of a set of clinical and radiological criteria for screening of COVID-19 using RT-PCR as the reference standard - Dataset
<p>Dataset of a cohort whose summary is described below.</p> <p>Abstract</p> <p><strong>Objective:</strong> To evaluate the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of a set of clinical-radiological criteria for COVID-19 screening in patients with severe acute respiratory failure (SARF) admitted to intensive care units (ICUs), using reverse-transcriptase polymerase chain reaction (RT-PCR) as the reference standard. <strong>Method: </strong>Diagnostic accuracy study including a historical cohort of 1009 patients consecutively admitted to ICUs across six hospitals in Curitiba (Brazil) from March to September, 2020. The sample was stratified into groups by the strength of suspicion for COVID-19 (strong <em>versus</em> weak) using parameters based on three clinical and radiological (chest computed tomography) criteria. The diagnosis of COVID-19 was confirmed by RT-PCR (referent). <strong>Results:</strong> With respect to RT-PCR, the proposed criteria had 98.5% (95% confidence interval [95% CI] 97.5–99.5%) sensitivity, 70% (95% CI 65.8–74.2%) specificity, 85.5% (95% CI 83.4–87.7%) accuracy, PPV of 79.7% (95% CI 76.6–82.7%) and NPV of 97.6% (95% CI 95.9–99.2%). <strong>Conclusion: </strong>The proposed set of clinical-radiological criteria were accurate in identifying patients with strong <em>versus</em> weak suspicion for COVID-19 and had high sensitivity and considerable specificity with respect to RT-PCR. These criteria may be useful for screening COVID-19 in patients presenting with SARF.</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.