Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,076
datasets available to search
ShareScore release 0.7.1
Dataset results
1,076 results for “metabarcoding”
Multi-locus DNA metabarcoding of western spotted skunk diet in the McKenzie River Ranger District of the Willamette National Forest from 2017-2019
There are increasing concerns about the declining population trends of small mammalian carnivores around the world. Their conservation and management is often challenging due to limited knowledge about their ecology and natural history. To address one of these deficiencies for western spotted skunks (Spilogale gracilis), we investigated their diet in the Oregon Cascades of the Pacific Northwest during 2017 –2019. We collected 130 spotted skunk scats opportunistically and with detection dog teams and identified prey items using DNA metabarcoding and mechanical sorting. Western spotted skunk diet consisted of invertebrates such as wasps, millipedes, and gastropods, vertebrates such as small mammals, amphibians, and birds, and plants such as Gaultheria, Rubus, and Vaccinium. Diet also consisted of items such as black-tailed deer that were likely scavenged. Comparison in diet by season revealed that spotted skunks consumed more insects during the dry season (June –August), particularly wasps (75% of scats in the dry season), and marginally more mammals during the wet season(September –May). We observed similar diet in areas with no record of human disturbance and areas with a history of logging at most spatial scales, but scats collected in areas with older forest within a skunk’s home range (1 km buffer) were more likely to contain insects. Western spotted skunks provide food web linkages between aquatic, terrestrial, and arboreal systems and serve functional roles of seed dispersal and scavenging. Due to their diverse diet and prey-switching, western spotted skunks may dampen the effects of irruptions of prey, such as wasps during dry springs and summers. By studying the natural history of western spotted skunks in the Pacific Northwest forests while they are still abundant, we provide key information necessary to achieve the conservation goal of keeping this common species common.
Arctic-boreal bryophyte dynamics since the last glacial from ancient DNA metabarcoding
<p>A total of 26 lake-sediment cores collected from 26 study sites spanning the glacial and interglacial transition are used in this study. These sites are distributed across Siberia, Beringia, and Alaska regions, with a gradient of vegetation types dominated by tundra in the northern region and transitioning to boreal forest in the southern extents. DNA samples from the sediment core were analysed with a standard sedimentary ancient DNA metabarcoding pipeline (see additional description), which resulted in a raw dataset of all DNA plant sequences, which were then filtered for Bryophytes (Bryophyte DNA dataset). The Bryophyte DNA dataset contains 120 unique ASV. Samples in the Bryophyte DNA dataset are then grouped into 1000-year time slices and are subsequently resampled to a base count of 500 read counts for each time slice. After that, a Bryophyte trait datastet is assigned to the Bryophyte DNA dataset. </p> <p> </p> <h3>Input files</h3> <ul> <li><strong>Excel file with all data used in the R-Script:</strong> "Bryophytes_data.xlsx"</li> <li><strong>WorldClim 2.0 dataset with mean temperatures of Warmest Quarter</strong> (https://www.worldclim.org/; Fick, S.E. and R.J. Hijmans, 2017. WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. <a href="https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/joc.5086">International Journal of Climatology 37 (12): 4302-4315</a>): "wc2.1_30s_bio_10.tif"</li> </ul> <h3>R script</h3> <ul> <li><strong>R-Script:</strong> "2025-01-14_R-Script_ arctic_boreal_bryophyte_dynamics_DNA_metabarcoding.R"</li> </ul> <h3>R outputs</h3> <ul> <li><strong>resampled Bryophyte metabarcoding percentage dataset with ASV:</strong> "2025-01-14_bryophyta_resampled_percentages_mean_100runs_sequences.csv"</li> <li><strong>resampled Bryophyte metabarcoding percentage dataset with unique scientific names: </strong>"2025-01-14_bryophyta_resampled_percentages_mean_100runs_scientific_names.csv"</li> <li><strong>GBIF taxa occurrences with WorldClim temperature data: </strong>"2025-01-14_gbif_taxa_occurrences_seqtypes_climate.csv"</li> </ul> <p> </p>
18S V9 metabarcoding reference databases and naive-bayes classifier
<p>18S metabarcoding databases and naive-bayes classifiers specific to the V9 region. Built from the <a href="https://pr2-database.org/">PR2 database</a> using Qiime2 (version 2023.2)<a href="https://github.com/BenKaehler/q2-clawback">.</a> Includes a naive-bayes classifier for use with Qiime2. Sequences were dereplicated with Rescript --p-mode 'uniq' , retaining identical sequence records that have differing taxonomies.</p><p>Primers used:</p><p>EMP 18S 1391f: GTACACACCGCCCGTC</p><p>EMP 18S EukBr: TGATCCTTCTGCAGGTTCACCTAC</p><p><strong>Stats</strong></p><p>19,470 unique sequences</p><p>39,170 total sequences</p><p>11,748 unique taxa </p><p>Note: there were 221,085 sequences in the original PR2 database. Many were filtered out due to the in-silico extraction with our V9 primers.</p><h3>File Descriptions</h3><p><strong>Files in bold are recommended for taxonomic classification.</strong></p><p>Create naive-bayes classifier for 18S PR2 database.md: Markdown with code used to generate databases |</p><p><strong>pr2_v5.0.0_SSU_18S-V9_uniq-classifier.qza</strong>: Unweighted naive-bayes classifier for 18S V9 (primers 1391f, EukBr), extracted from PR2 v5.0.1, dereplicated, generated by qiime2-2023.2 |</p><p><strong>pr2_version_5.0.0_SSU_18S-V9_uniq_seqs.qza</strong>: Sequences for 18S V9 (primers 1391f, EukBr), extracted from PR2 v5.0.1, dereplicated, generated by qiime2-2023.2 |</p><p><strong>pr2_version_5.0.0_SSU_18S-V9_uniq_tax.qza</strong>: Taxa for pr2_version_5.0.0_SSU_18S-V9_uniq_seqs.qza (dereplicated) |</p><p>pr2_version_5.0.0_SSU_18S-V9_seqs.qza: Sequences for 18S V9 (primers 1391f, EukBr), extracted from PR2 v5.0.1, NOT dereplicated, generated by qiime2-2023.2 |</p><p>pr2_version_5.0.0_SSU_18S-V9_tax.qza: Taxa for pr2_version_5.0.0_SSU_18S-V9_seqs.qza (NOT dereplicated) </p><p>pr2_version_5.0.0_SSU_mothur.fasta: SSU sequences downloaded from PR2 v 5.0.1 |</p><p>pr2_version_5.0.0_SSU_mothur.tax: SSU taxa downloaded from PR2 v5.0.1 |</p><p>pr2_version_5.0.0_taxonomy.xlsx: Detailed taxonomy downloaded from PR2 v5.0.1 |</p>
12S metabarcoding reference data from the Research Institute for Nature and Forest (INBO)
<p>This dataset contains metabarcoding reference data with 12S sequences from fish and other vertebrates for Teleo and Riaz primers suited for use with the <a href="https://git.metabarcoding.org/obitools/obitools/wikis/home/">OBITools</a> package.</p> <p><strong>Files</strong><br>- <strong>all_seqs_INBO_riaz_amplified.fasta</strong>: reference data for Riaz marker<br>- <strong>all_seqs_INBO_Valentini_teleo_amplified.fasta</strong>: reference data for Teleo marker<br>- <strong>species_INBO_riaz.csv</strong>: species for which reference data is included in Riaz dataset<br>- <strong>species_INBO_teleo.csv</strong>: species for which reference data is included in Teleo dataset</p>
Metabarcoding reveals a high diversity of woody host-associated Phytophthora spp. in soils at public gardens and amenity woodlands in Britain
<p>This is the demultiplexed Illumina MiSeq raw sequencing data from two 96-well plates from the following recent publication, shared with permission of the corresponding author, Sarah Green:</p> <p>Riddell <em>et al.</em> (2019). Metabarcoding reveals a high diversity of woody host-associated <em>Phytophthora</em> spp. in soils at public gardens and amenity woodlands in Britain. https://doi.org/10.7717/peerj.6931<br> <br> It consists of 244 gzipped compressed plain text FASTQ format sequence files, grouped into 122 pairs by the widely used R1 and R2 suffix. The files have been renamed to use the anonymised site numbers (1 to 14) as in the paper, see also supplementary table one for site metadata. Additionally there are two negative controls, and positive control DNA mixtures of 10 and 15 species as described in the paper.<br> </p>
Metabarcoding data (number of reads per operational taxonomic unit) from a monitoring study of sandy beach meiofauna before and after sand nourishment (Ahrenshoop, Baltic Sea)
<p>We provide metabarcoding data (number of reads per operational taxonomic unit, OTU) determined from sediment samples collected on the sandy-beach water line of Ahrenshoop (Baltic Sea). Five sampling stations lay within the zone impacted by the sand nourishment between the boundary of the nature reserve in the north east and a site just north of the breakwater (AH01–AH05). An unaffected reference station was located south of Ahrenshoop (close to Niehagen) at the end of the road Pappelallee (PAP). Samples were collected at four dates. The first sampling was carried out before the sand nourishment took place (T0: 14 and 16 September 2021). Three samplings were realised after the impact: T1 (23 March 2022), T2 (27 September 2022), and T3 (28 March 2023). Latitude and longitude of each sampling location per station were recorded at each sampling date using a hand-held GPS application on a mobile phone. At the stations sampling locations varied over time. Prior to the sand nourishment the beach was narrow due to sand erosion in previous years. After the nourishment the additional extent of the beach was approximately 40 m at sampling date T1. Subsequently, progressive sand erosion forced the sampling locations (situated at the water line) further inland at T2 and T3.<br>Samples were taken from the beach-water interface (water line) in the middle of the area between two groynes. Plexiglass cores (inner core diameter 5.4 cm) were inserted vertically into the sediment down to 15 cm depth. Each core was sliced in 5 cm-layers (0–5, 5–10 and 10–15 cm). Sediment horizons were preserved in 96–99% ethanol. <br>Three cores (2 cores at T0) per sampling date were taken for metabarcoding analyses. The organisms were extracted by decantation over a 32-μm sieve. Genomic DNA was extracted from the filters using the DNeasy PowerSoil pro kit (Qiagen). Realtime-PCR was performed to amplify V1&V2, two hypervariable regions of 18S rDNA gene. The sequencing run was performed using the MiSeq Reagent Nanokit v2 (250 cycles paired end) on an Illumina MiSeq platform at the DZMB Metabarcoding lab in Wilhelmshaven, Germany. High-resolution amplicon sequence variants (ASVs) were obtained and compared to the NCBI database to assign taxonomic information to each ASV. The target meiofauna ASVs were further classified into operational taxonomic units (OTUs) with a 3% cut-off threshold using the statistical software R.</p> <p>Here, we present two Tables (as xlsx and tab-delimited files):<br>(1) the taxonomic description of the 843 OTUs and their assigned ID number;<br>(2) the number of reads per OTU per sample (including metadata for each sample: event; date; latitude; longitude; station, core and sample ID; sediment depth).</p> <p>The metabarcoding data are part of a larger ecological study on the influence of sand nourishment on meiofauna communities, which included grain-size and meiofauna abundances (see “related works”).</p> <p><strong>Comment: </strong>Our study is related to but not funded by the project ECAS Baltic: Strategies of ecosystem-friendly coastal protection and ecosystem-supporting coastal adaptation for the German Baltic Sea Coast <a href="https://deutsche-kuestenforschung.de/ecas-baltic.html">https://deutsche-kuestenforschung.de/ecas-baltic.html</a></p>
The tpm metabarcoding DNA sequence database for taxonomic allocations using RDP classifier implemented in DADA2.
<p><strong>The </strong><em>tpm</em><strong> metabarcoding DNA sequence database for taxonomic allocations using the Mothur and DADA2 bio-informatic tools</strong></p> <p>A.C.M. Pozzi<sup>1</sup>, R. Bouchali<sup>1</sup>, L. Marjolet<sup>1</sup>, B. Cournoyer<sup>1</sup></p> <p><sup>1 </sup><em>University of Lyon, UMR Ecologie Microbienne Lyon (LEM), CNRS 5557, INRAE 1418, Université Claude Bernard Lyon 1, VetAgro Sup, Research Team “Bacterial Opportunistic Pathogens and Environment” (BPOE), 69280 Marcy L’Etoile, France.</em></p> <p><strong>Corresponding authors: </strong></p> <ul> <li>A.C.M. Pozzi, UMR Microbial Ecology, CNRS 5557, CNRS 1418, VetAgro Sup, Main building, aisle 3, 1st floor, 69280 Marcy-L’Etoile, France. Tel. (+33) 478 87 39 47. Fax. (+33) 472 43 12 23. Email: <a href="mailto:adrien.meynier_pozzi@vetagro-sup.fr">adrien.meynier_pozzi@vetagro-sup.fr</a></li> <li>B. Cournoyer, UMR Microbial Ecology, CNRS 5557, CNRS 1418, VetAgro Sup, Main building, aisle 3, 1st floor, 69280 Marcy-L’Etoile, France. Tel. (+33) 478 87 56 47. Fax. (+33) 472 43 12 23. Email: and <a href="mailto:benoit.cournoyer@vetagro-sup.fr">benoit.cournoyer@vetagro-sup.fr</a></li> </ul> <p><strong>Keywords:</strong></p> <p>BACtpm, Bacteria, <em>tpm</em>, thiopurine-<em>S</em>-methyltransferase EC:2.1.1.67, Nucleotide sequences, PCR products, Next-Generation-Sequencing, OTHU</p> <p><strong>Description:</strong></p> <ul> <li>The <em>tpm</em> gene codes for the thiopurine-<em>S</em>-methyltransferase (TPMT), an enzyme that can detoxify metalloid-containing oxyanions and xenobiotics (Cournoyer et al., 1998). Bacterial TPMTs radiated apart from human and animal TPMTs, and showed a vertical evolution in line with the 16S rRNA gene molecular phylogeny (Favre‐Bonté et al., 2005).</li> <li>The <em>tpm</em> database, named BACtpm, was designed to apply the <em>tpm</em>-metabarcoding analytical scheme published in Aigle et al. (2021). It includes the full <em>tpm</em> identifiers, GenBank accession numbers, complete taxonomic records (domain down to strain code) of about 215 nucleotide-long <em>tpm</em> sequences of 840 unique taxa belonging to 139 genera.</li> <li>Nucleotide sequences of <em>tpm</em> (range: 190-233 nucleotides) were either retrieved from public repositories (GenBank) or made available by B. Cournoyer’s research group. Colin et al. (2020) described the PCR and high throughput Illumina Miseq DNA sequencing procedures used to produce <em>tpm</em> sequences.</li> <li>BACtpm v.2.0.1 (June 2021 release) is made available under the Creative Commons Attribution 4.0 International Licence. It can be used for the taxonomic allocations of <em>tpm </em>sequences down to the species and strain levels. Data is stored in the csv format enabling future user to reformat it to fit their specific needs.</li> </ul> <p><strong>Acknowledgments:</strong></p> <p>We thank the worldwide community of microbiologists who made contributions to public databases in the past decades, and made possible the elaboration of the BACtpm database. We also thank the Field Observatory in Urban Hydrology (OTHU, <a href="http://www.graie.org/othu/">www.graie.org/othu/</a>), Labex IMU (Intelligence des Mondes Urbains), the Greater Lyon Urban Community, the School of Integrated Watershed Sciences H2O'LYON, and the Lyon Urban School for their support in the development of this database. This work was funded by the French national research program for environmental and occupational health of ANSES under the terms of project “Iouqmer” EST 2016/1/120, l'Agence Nationale de la Recherche through ANR-16-CE32-0006, ANR-17-CE04-0010, ANR-17-EURE-0018 and ANR-17-CONV-0004, by the MITI CNRS project named Urbamic, and the French water agency for the Rhône, Mediterranean and Corsica areas through the Desir and DOmic projects. We thank former BPOE lab members who contributed to start and expand the BACtpm database: Céline COLINON, Romain MARTI, Emilie BOURGEOIS, Sébastien RIBUN and Yannick COLIN.</p> <p><strong>References:</strong></p> <p>Aigle, A., Colin, Y., Bouchali, R., Bourgeois, E., Marti, R., Ribun, S., Marjolet, L., Pozzi, A.C.M., Misery, B., Colinon, C., Bernardin-Souibgui, C., Wiest, L., Blaha, D., Galia, W., Cournoyer, B., 2021. Spatio-temporal variations in chemical pollutants found among urban deposits match changes in thiopurine S-methyltransferase-harboring bacteria tracked by the tpm metabarcoding approach. Sci. Total Environ. 767, 145425. https://doi.org/10.1016/j.scitotenv.2021.145425</p> <p>Colin, Y., Bouchali, R., Marjolet, L., Marti, R., Vautrin, F., Voisin, J., Bourgeois, E., Rodriguez-Nava, V., Blaha, D., Winiarski, T., Mermillod-Blondin, F., Cournoyer, B., 2020. Coalescence of bacterial groups originating from urban runoffs and artificial infiltration systems among aquifer microbiomes. Hydrol. Earth Syst. Sci. 24, 4257–4273. https://doi.org/10.5194/hess-24-4257-2020</p> <p>Cournoyer, B., Watanabe, S., Vivian, A., 1998. A tellurite-resistance genetic determinant from phytopathogenic pseudomonads encodes a thiopurine methyltransferase: evidence of a widely-conserved family of methyltransferases1The International Collaboration (IC) accession number of the DNA sequence is L49178.1. Biochim. Biophys. Acta BBA - Gene Struct. Expr. 1397, 161–168. https://doi.org/10.1016/S0167-4781(98)00020-7</p> <p>Favre‐Bonté, S., Ranjard, L., Colinon, C., Prigent‐Combaret, C., Nazaret, S., Cournoyer, B., 2005. Freshwater selenium-methylating bacterial thiopurine methyltransferases: diversity and molecular phylogeny. Environ. Microbiol. 7, 153–164. https://doi.org/10.1111/j.1462-2920.2004.00670.x</p>
Machine learning classifiers for species classification of fungi using error-prone long-reads on extended metabarcodes
<p>Machine learning models used in the decision tree of linked machine learning models (<a href="https://github.com/teenjes/fungal_ML">https://github.com/teenjes/fungal_ML</a>)</p>
Construction, validation and application of nocturnal pollen transport networks in an agro-ecosystem: datasets collected using light microscopy and DNA metabarcoding
<p>This dataset contains all data required to reproduce the analyses conducted in Macgregor <em>et al. </em>(2018), using the R Notebook archived at doi: <a href="https://dx.doi.org/10.5281/zenodo.1322712">10.5281/zenodo.1322712</a>.</p> <p>Specifically, the dataset contains details of pollen transport detected on two matched samples, each containing 311 moths of 41 species, using two methods: a traditional light microscopy approach and a novel DNA metabarcoding approach. Both raw and manually-curated versions of each dataset are archived for full clarity. The dataset additionally contains all metadata required to fully interpret these data, including the RGB tables used to prepare Fig 4 in Macgregor <em>et al. </em>(2018).</p> <p>Macgregor <em>et al. </em>(2018) Construction, validation and application of nocturnal pollen transport networks in an agro-ecosystem: a comparison using light microscopy and DNA metabarcoding. <em>Ecological Entomology</em>, doi: <a href="https://dx.doi.org/10.1111/een.12674">10.1111/een.12674</a>.</p>
Data from: Andriollo T., Gillet F., Michaux J.R., Ruedi M. (2019). The menu varies with metabarcoding practices: A case study with the bat Plecotus auritus. PLoS ONE 14(7)
<p><strong>Supporting data for: </strong>Andriollo T., Gillet F., Michaux J.R., Ruedi M. (2019). The menu varies with metabarcoding practices: a case study with the bat <em>Plecotus auritus</em>. PLoS ONE 14(7): e0219135. https://doi.org/10.1371/journal.pone.0219135</p> <p>Raw DNA sequences of prey of <em>Plecotus auritus</em>. Sampling information separated by semicolums as folows:</p> <p>>Sequence number; Colony; Date; Sample name; Dataset; Is the sequence attributable to the diet or not (Diet); Read numbers (Size); DNA sequence</p>
Spineless and overlooked: DNA metabarcoding of autonomous reef monitoring structures reveals intra- and interspecific genetic diversity in Mediterranean invertebrates
<p>Sequence data and stepwise pipeline outputs associated with the article "Spineless and overlooked: DNA metabarcoding of autonomous reef monitoring structures reveals intra- and interspecific genetic diversity in Mediterranean invertebrates".</p> <p>Preprint available here: <a href="https://doi.org/10.22541/au.167085544.47638352/v1">10.22541/au.167085544.47638352/v1</a></p> <p>Sequence data is deposited in fastq-format in folders by region (Palinuro.tar.gz, Livorno.tar.gz, and Rovinj.tar.gz) and a separate folder for controls (Controls.tar.gz). Each fastq-file contains sequences for a single PCR replicate named by sample and replicate number. Sample names are described in spineless_sample_names.csv. Positive control sequences are described in SM1_positive_controls.csv. Stepwise pipeline outputs are available in the folder Pipeline_outputs_stepwise.zip</p> <p>Scripts used to generate pipeline outputs as well as other aspects of the final article are available at <a href="https://github.com/thomasdotter/spineless-haplotypes">https://github.com/thomasdotter/spineless-haplotypes</a>.</p> <p> </p>
MURI Metabarcoding Sample Dataset
<p>This is a test dataset for the metabarcoding_QAQC_pipeline repo: <a href="https://github.com/MMARINeDNA/metabarcoding_QAQC_pipeline">https://github.com/MMARINeDNA/metabarcoding_QAQC_pipeline</a> . These are fastq files of eDNA metabarcoding sequences that were sequenced on the Illumina Miseq platform, as well as the Miseq sample sheet.</p>
16S V4-V5 metabarcoding reference databases and weighted naive-bayes classifiers, dereplicated
<p>16S metabarcoding databases and naive-bayes classifiers specific to the V4-V5 region. Built from the <a href="https://www.arb-silva.de/documentation/release-138/">Silva 138.1 SSU Ref NR 99</a> database using Qiime2 (version 2023.2) and the <a href="https://github.com/BenKaehler/q2-clawback">q2-clawback plugin.</a> Includes weighted classifiers for two Earth Microbiome Project Ontology (EMPO) 3 habitat types: "sediment (saline)" and "water (saline)" , with data downloaded from <a href="https://qiita.ucsd.edu/">Qiita</a>. Sequences were dereplicated with Rescript --p-mode 'uniq' , retaining identical sequence records that have differing taxonomies.</p> <p>Primers used:</p> <p>EMP 16S 515f: GTGYCAGCMGCCGCGGTAA</p> <p>EMP 16S 926r: CCGYCAATTYMTTTRAGTTT</p> <p><strong>Stats</strong></p> <p>286,948 unique sequences</p> <p>309,567 total sequences</p> <p>46,254 unique taxa (Level 7)</p> <table> <caption>File description</caption> <thead> <tr> <th scope="col"> <table> <thead> <tr> <th>File</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>make new 16S silva V4-V5 database.md</td> <td>Markdown with code used to generate databases</td> </tr> <tr> <td>silva-138-99-seqs.qza</td> <td>Full length Silva 138.1 SSU 99 sequences</td> </tr> <tr> <td>silva-138-99-tax.qza</td> <td>Taxa for full length Silva 138.1 SSU 99 database</td> </tr> <tr> <td>silva-138_1-99-515f_926r-uniq-seqs.qza</td> <td>Sequences for 16S V4-V5 (primers 515f, 926r), extracted from Silva 138.1 SSU 99, generated by qiime2-2023.2 (forward compatible), dereplicated</td> </tr> <tr> <td>silva-138_1-99-515f_926r-uniq-taxa.qza</td> <td>Taxa for silva-138_1-99-515f_926r-seqs.qza database, dereplicated</td> </tr> <tr> <td>uniform-silva-138_1-99-515f_926r-uniq-classifier.qza</td> <td>Unweighted (uniform) naive-bayes classifier for 16S V4-V5 (primers 515f, 926r) extracted from Silva 138.1 SSU 99, generated by qiime2-2023.2 (forward compatible)</td> </tr> <tr> <td>silva-138_1-99-515f_926r-uniq-sediment-saline-classifier.qza</td> <td>Weighted naive-bayes classifier for 16S V4-V5 (primers 515f, 926r) extracted from Silva 138.1 SSU 99, weighted for sediment-saline, generated by qiime2-2023.2 (forward compatible)</td> </tr> <tr> <td>silva-138_1-99-515f_926r-q2_2023_2-uniq-sediment-saline-weights.qza</td> <td>Weights used to generate silva-138_1-99-515f_926r-q2_2023_2-sediment-saline-classifier.qza</td> </tr> <tr> <td>silva-138_1-99-515f_926r-uniq-water-saline-classifier.qza</td> <td>Weighted naive-bayes classifier for 16S V4-V5 (primers 515f, 926r) extracted from Silva 138.1 SSU 99, weighted for water-saline, generated by qiime2-2023.2 (forward compatible)</td> </tr> <tr> <td>silva-138_1-99-515f_926r-uniq-water-saline-weights.qza</td> <td>Weights used to generate silva-138_1-99-515f_926r-water-saline-classifier.qza</td> </tr> </tbody> </table> </th> <th scope="col"> </th> </tr> </thead> <tbody> </tbody> </table> <p> </p>
Data from: Deciphering host-parasitoid interactions and parasitism rates of crop pests using DNA metabarcoding
Open the record for dataset details and reuse information.
COI and 18S metabarcoding data from Hidden Lake (Banff National Park, Canada) over two rotenone applications between 2018 and 2020.
Water samples were taken in Hidden Lake at five different time points around two rotenone applications: (i) five weeks prior to the first rotenone application, on July 12 2018; (ii) approximately three weeks after the first application of rotenone, on 7 September 2018; (iii) approximately 10 months after the first rotenone application, on 10 July 2019; (iv) four weeks following the final treatment of rotenone on the 17 September 2019; and (v) one year after the final rotenone treatment, on 19 August 2020. For each time point there is a pelagic, a littoral and a profundal sample. COI and 18S metabarcoding methods were used to produce community data. The objective of this study was to assess the non-target effect of rotenone application (in summer 2018 and 2019) on aquatic communities (i.e. phytoplankton, fungi, zooplankton and benthic macroinvertebrates).
DNA metabarcoding and spatial modelling link diet diversification with distribution homogeneity in European bats
<p>Inferences of the interactions between species’ ecological niches and spatial distribution have been historically based on simple metrics such as low-resolution dietary breadth and range size, which might have impeded the identification of meaningful links between niche features and spatial patterns. We analysed the relationship between dietary niche breadth and spatial distribution features of European bats, by combining continent-wide DNA metabarcoding of faecal samples with species distribution modelling. Our results show that while range size is not correlated with dietary features of bats, the homogeneity of the spatial distribution of species exhibits a strong correlation with dietary breadth. We also found that dietary breadth is correlated with bats’ hunting flexibility. However, these two patterns only stand when the phylogenetic relations between prey are accounted for when measuring dietary breadth. Our results suggest that the capacity to exploit different prey types enables species to thrive in more distinct environments and therefore exhibit more homogeneous distributions within their ranges.</p>
Data from: DNA metabarcoding for biodiversity monitoring in a national park: screening for invasive and pest species
<ol> <li><span>DNA metabarcoding was utilized for a large-scale, multi-year assessment of biodiversity in Malaise trap collections from the Bavarian Forest National Park (Germany, Bavaria). </span></li> <li><span>Principal Component Analysis of read count-based biodiversities revealed clustering in concordance with whether collection sites were located inside or outside of the National Park.</span></li> <li><span>Jaccard distance matrices of the presences of BINs at collection sites in the two survey years (2016 and 2018) were significantly correlated.</span></li> <li><span>Overall similar patterns in the presence of total arthropod BINs, as well as BINs belonging to four major arthropod orders across the study area, were observed in both survey years, and are also comparable with results of a previous study based on DNA barcoding of Sanger-sequenced specimens.</span></li> <li><span>A custom reference sequence library was assembled from publicly available data to screen for pest or invasive arthropods among the specimens or from the preservative ethanol.</span></li> <li> <span>A single 98.6% match to the invasive bark beetle </span><span>Ips duplicatus</span><span> was detected in an ethanol sample. This species has not previously been detected in the National Park.</span> </li> </ol>
ASV Tables inferred by DADA2 from the TARA Oceans v9 metabarcoding dataset
<p>Tables of exact amplicon sequence variants (ASVs) were generated from the TARA Oceans metabarcoding data (~766 million reads from 334 plankton samples, V9 region of the 18S rRNA gene, DOI: 10.5281/zenodo.15600) by DADA2 on a 2016 Macbook Pro. The R script used to process the data is included, alongside 3 ASV tables: the observed ASVs before chimera removal (sta), after consensus chimera removal (st.consensus, recommended) and after pooled chimera removal (st.nochim).</p> <p>The ASV tables are available in two formats. The first format is as matrices (rows named by sample, columns named by sequence variant) stored in RDS format -- these can be read back into R with the readRDS command. The second format is as biom-format files (json).</p>
Summarised contextual data about metabarcoding Tara Oceans samples (2009-2013)
<p>Tab-separated values table describing the metabarcoding samples from the expedition Tara Oceans (2009-2013).</p> <p>Information such as depth, time, geographic position, size fraction, collected from <a href="https://pangaea.de/">Pangaea</a>, are listed in context_general tables. In context_stat tables, you will find a selection of physico-chemical parameters. Tara_Oceans_Pangaea_context.rds gathers all the data collected from Pangaea in a single R object.</p> <p>These tables have been built using the code here: <a href="https://gitlab.com/tara-and-friends-euk-metab/tara-oceans-metab-context/-/tree/v1.1.1" target="_blank" rel="noopener">https://gitlab.com/tara-and-friends-euk-metab/tara-oceans-metab-context/-/tree/v1.1.2</a> (v1.1.2).</p> <p>In this version 16S metabarcoding samples missing in previous versions were added in context_general.* and context_sats.*</p>
Introduction to Metabarcoding using QIIME2 - Echidna Dataset
<p>This is the data and associated metadata file for the Melbourne Bioinformatics workshop (https://mdhs.unimelb.edu.au/melbournebioinformatics/teaching-and-training), Introduction to Metabarcoding using QIIME2 - Mammal Dataset. There are 38 samples in this dataset from the short-beaked echidna <a href="https://en.wikipedia.org/wiki/Short-beaked_echidna"><i>Tachyglossus aculeatus. </i></a></p><ol><li>5 samples from each individual (3x male and 3x female).</li><li>8 control samples (DNA extraction blanks (n=5) and PCR blanks (n=3))</li></ol>
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.