Find research datasets worth reusing

opencc-by-4.0Nov 2022View details →

DADA2 formatted eHOMD 16S rRNA gene sequences databse

eHOMD Refseq database (V15.22) formated to be used with dada2 i.e., dada2::assignTaxonomy(seqs, "eHOMD_RefSeq_dada2_V15.22.fasta.gz" ) and dada2::addSpecies(taxa, "eHOMD_RefSeq_dada2_assign_species_V15.22.fasta.gz", verbose=TRUE) Alternatively, you could use the metabaRpipe R package to directly update the taxonomy of a phyloseq object see: https://github.com/fconstancias/metabaRpipe#2-addingreplacing-taxonomical-table-in-a-phyloseq-object Example below: source("https://raw.githubusercontent.com/fconstancias/metabaRpipe-source/master/Rscripts/functions.R") readRDS("dada2/phyloseq.RDS") %>%   phyloseq_dada2_tax(physeq = .,                      threshold = 60,                      db ="~/metabaRpipe/databases/eHOMD_RefSeq_dada2_V15.22.fasta.gz",                      db_species ="~/metabaRpipe/databases/eHOMD_RefSeq_dada2_assign_species_V15.22.fasta.gz",                      nthreads = 2,                      full_return = FALSE) -> physeq_eHOMD_tax

opencc-by-4.0Aug 2022View details →

Harmonised LUCAS database classified by crop sequence type

Assessing the benefits of crop diversification – a pillar of the agroecological transition – on a large scale requires a description of current crop sequences as a baseline, which is lacking at the scale of the European Union (EU). This work is based on the Harmonised LUCAS in-situ land cover and use database for field surveys from 2006 to 2018 in the European Union (doi: <a href="http://doi.org/10.2905/f85907ae-d123-471f-a44a-8cca993485a2">10.2905/f85907ae-d123-471f-a44a-8cca993485a2)</a> to fill this gap, We completed this dataset with a crop sequence type information for each point under non-perennial agricultural land cover in 2012, 2015 and 2018. The dataset lucas_classified.csv includes 31 159 points. Variables "point_id", "nuts0", "nuts2", "th_lat", "th_long", "LC1_2012", "LC1_2015", "LC1_2018" are inherited from the Harmonised LUCAS databse. Variables "cereals", "corn", "rapeseed", "sunflower", "pulses", "rootCrops", "forageLeg", "grassland" correspond to the temporal frequencies of respectively cereals, corn, rapeseed, sunflower, pulses, root crops, forage legumes and grassland within the 2012, 2015 and 2018 crop sequence for each point. Variable "crop_sequence_type" is the crop sequence type assigned to each point, among eight options: cereals, corn and cereals, forage legumes and cereals, pulses and cereals, rapeseed and cereals, root crops and cereals, sunflower and cereals, temporary grasslands. This dataset could be used to map current dominant crop sequences in the European Union, as illustrated in the map attached, and to assess the benefits of future crop diversification.

opencc-by-4.0Aug 2023View details →

[Luminescence Dataset] The loess sequence of Dolní Věstonice, Czech Republic: A new OSL‐based chronology of the last climatic cycle

Dolní Věstonice - Luminescence Dataset <table> <tbody> <tr> <td> Applicable Licence </td> <td> CC-BY-NC </td> </tr> <tr> <td> Data curators: </td> <td> Markus Fuchs, Sebastian Kreutzer </td> </tr> <tr> <td> Reference original work </td> <td> Fuchs, M., Kreutzer, S., Rousseau, D. D., Antoine, P., Hatté, C., Lagroix, F., Moine, O., Gauthier, C., Svoboda, J., and Lisá, L.: The loess sequence of Dolní Věstonice, Czech Republic: A new OSL‐based chronology of the last climatic cycle, Boreas, 42, 664–677, https://doi.org/10.1111/j.1502-3885.2012.00299.x, 2013. </td> </tr> <tr> <td> How to refer/cite this dataset </td> <td> Please the information auto-generated by Zendo </td> </tr> </tbody> </table> Scope This dataset contains the original luminescence data used to create the chronostratigraphy of the Dolní Věstonice loess profile. By publishing this dataset, we aim to support studies on the general characteristics of luminescence behaviours of natural minerals and support the FAIR guidelines for data sharing. This data is primary unmodified measurement data with all the possible errors and typos! Please note: The dataset was compiled with the greatest care. However, the dataset comes without any guarantee. Human errors are always possible. If you feel that, after reading the original study and this document, the metadata describing the dataset is insufficient, please contact the data curators so that this document can be updated accordingly. Contrary, the primary cannot be updated/modified! Dataset structure The dataset consists of sequence files (<code>SEQ</code>) and measurement data (<code>BIN</code>). To each <code>.bin</code> file, there should be one corresponding sequence file. Measurement data are tagged with sample names in the <code>.bin</code> file, e.g., unless, in case of mistakes, samples can be distinguished in the file.  <ul> <li> <code>...BIN/</code> <ul> <li> <code>BIN/A-VALUE/:</code>measurement data with a-value measurements; the alpha irradiation was partly done on an external source </li> <li> <code>BIN/MAIN/</code> measurement data with data used to estimate the equivalent dose of each sample </li> <li> <code>BIN/PREHEAT_PLATEAU</code> files with combined preheat and dose recovery test results </li> <li> <code>BIN/MISC</code> various additional measurements as indicated by the name </li> </ul> </li> <li> <code>...SEQ/</code> <ul> <li> <code>SEQ/A-VALUE/</code> </li> <li> <code>SEQ/MAIN</code> </li> <li> <code>SEQ/PREHEAT_PLATEAU</code> </li> <li> <code>SEQ/MISC</code> </li> </ul> </li> <li> <code>...DE_CSV_EXPORT</code> The extracted equivalent dose values from the measurements with uncertainties in s.  </li> </ul> Please note that samples were partly measured on different machines. All files are selections; in the course of the study, we carried out various additional measurements, in particular, preliminary tests; those data are not included in the dataset to keep the dataset comprehensible.  Used abbreviations Abbreviations as used in the measurement and sequence file names. For technical details, we refer to the original study and the reference therein. <table> <tbody> <tr> <td> ABBREVIATION </td> <td> TERM </td> <td> DESCRIPTION </td> </tr> <tr> <td> <code>CG</code> </td> <td> coarse grain </td> <td> refers to the used grain size fraction, here: 90-200 µm </td> </tr> <tr> <td> <code>DRT</code> </td> <td> dose-recovery test </td> <td> measurement data with dose recovery test results; in this study, such measurements were carried out in combination with different preheat settings </td> </tr> <tr> <td> <code>FG</code> </td> <td> fine grain </td> <td> refers to the used grain size fraction, here: 4-11 µm </td> </tr> <tr> <td> <code>IRSLT</code> </td> <td> Infrared stimulated test </td> <td> test for feldspar contamination </td> </tr> <tr> <td> <code>LM</code> </td> <td> linear modulation </td> <td> linearly modulated optically stimulated luminescence measurements </td> </tr> <tr> <td> <code>MAIN</code> </td> <td> main measurement </td> <td> usually, the measurement used to estimate the equivalent dose </td> </tr> <tr> <td> <code>MG</code> </td> <td> medium grain </td> <td> refers to the used grain size fraction, here: 38-63 µm </td> </tr> <tr> <td> <code>mz</code> </td> <td> Moritz </td> <td> Name of the used Risø OSL/TL DA-15 reader </td> </tr> <tr> <td> <code>mx</code> </td> <td> Max </td> <td> Name of the used Risø OSL/TL DA-15 reader </td> </tr> <tr> <td> <code>NEU</code> </td> <td> neu </td> <td> a German word translating to 'new' </td> </tr> <tr> <td> <code>oDRT</code> </td> <td> oDRT </td> <td> a typo for just <code>DRT</code> </td> </tr> <tr> <td> <code>PreaHeat</code> </td> <td> preheat test </td> <td> test against different preheat temperatures </td> </tr> <tr> <td> <code>Q</code> </td> <td> quartz </td> <td> the measured mineral composition, often in combination with <code>CG</code> , <code>MG</code> , or F<code>G</code> . Example: <code>FGQ</code> : fine grain quartz </td> </tr> </tbody> </table>

opencc-by-nc-sa-4.0Sep 2023View details →

Magnetic resonance spectroscopy data acquired in tinnitus subjects and healthy volunteers using PRESS sequence

This dataset contains raw free induction decay (FID) signals collected during 1H magnetic resonance spectroscopy (MRS) study in 52 individuals with tinnitus (24 with unilateral and 28 with bilateral tinnitus) and 25 healthy volunteers (described in detail in a separate article doi:10.1038/s41598-023-45024-3).Data acquisition was performed using 3T Siemens Prisma Fit scanner with a 20-channel receiver head-coil. A single voxel spectroscopy (SVS) PRESS (Point-Resolved Spectroscopy Sequence) sequence was applied for collection of MRS data, using standard Siemens water suppression (water saturation, 50 Hz bandwidth) and no lipid suppression. MRS data was collected from four cubic 3.75 cm3 (1.5 cm x 1.5 cm x 1.5 cm) regions-of-interest in the brain, placed in the left temporal lobe, right temporal lobe, left frontal lobe, and right frontal lobe. The MRS sequence parameters were: TR (time of repetition) = 2000 ms, TE (time of echo) = 40 ms, TA (time of acquisition) = 4 min 26 s, 128 averages with 1024 time points and 1200 Hz bandwidth.MRS data is stored in RDA file format, developed by Siemens (see doi:10.1002/nbm.4257, Table 1). Each RDA file contains a text header (which can be viewed using a standard notepad application) and binary FID signal under the header. Data can be imported for analysis using several open-source packages (tested with FID-A doi:10.1002/mrm.26091 and spant doi:10.21105/joss.03646). Naming scheme of files is as follows:<participant ID>_<hemisphere: L or R>_<region: F (frontal) or T (temporal)>.rdaFor example: 001_L_F.rda is data from participant 001 collected from a voxel placed in a ROI in the left frontal lobe.In order to allow replication of the results from the original article, we also added information about the group of each of the subjects. This information is stored in a TSV file containing two columns: participant_ID and group (C – control, TU – unilateral tinnitus, TB – bilateral tinnitus).Aside from replication of our results this dataset may be used e.g. for testing of different MRS data processing pipelines.

opencc-by-4.0Jun 2021View details →

The tpm metabarcoding DNA sequence database for taxonomic allocations using RDP classifier implemented in DADA2.

The tpm metabarcoding DNA sequence database for taxonomic allocations using the Mothur and DADA2 bio-informatic tools A.C.M. Pozzi1, R. Bouchali1, L. Marjolet1, B. Cournoyer1 1 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. Corresponding authors: <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> Keywords: BACtpm, Bacteria, tpm, thiopurine-S-methyltransferase EC:2.1.1.67, Nucleotide sequences, PCR products, Next-Generation-Sequencing, OTHU Description: <ul> <li>The tpm gene codes for the thiopurine-S-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 tpm database, named BACtpm, was designed to apply the tpm-metabarcoding analytical scheme published in Aigle et al. (2021). It includes the full tpm identifiers, GenBank accession numbers, complete taxonomic records (domain down to strain code) of about 215 nucleotide-long tpm sequences of 840 unique taxa belonging to 139 genera.</li> <li>Nucleotide sequences of tpm (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 tpm 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 tpm 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> Acknowledgments: 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. References: 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 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 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 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

opencc-by-4.0May 2019View details →

Inferring whole-genome histories in large population datasets: inferred tree sequences for 1000 Genomes

Tree sequences inferred for the 1000 Genomes phase 3 autosomes using <a href="https://tsinfer.readthedocs.io/">tsinfer</a> version 0.1.4 and compressed using <a href="https://tszip.readthedocs.io/en/stable/">tszip</a>. Tree sequences can  be decompressed as follows: <pre><code class="language-bash">$ tsunzip 1kg_chr1.trees.tsz</code></pre> Once decompressed, trees files can be loaded and processed using <a href="https://tskit.readthedocs.io">tskit</a>.  <pre><code class="language-python">import tskit ts = tskit.load("1kg_chr1.trees") # ts is an instance of tskit.TreeSequence print("Chromosome 1 contains {} trees".format(ts.num_trees))</code></pre> Metadata associated with individuals and populations was derived from the original <a href="http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/working/20130606_sample_info/20130606_g1k.ped">source</a> and converted to JSON form. For example, to access individual metadata we can use: <pre><code class="language-python">import tskit import json ts = tskit.load("1kg_chr1.trees") ind = ts.individual(0) metadata_dict = json.loads(ind.metadata)</code></pre> The metadata_dict variable will now contain all the metadata for the individual with ID 0 as a dictionary. Metadata associated with populations can be found in a similar way. Population IDs are associated with individuals via their constituent nodes. For example, <pre><code class="language-python">pop_metadata = [json.loads(pop.metadata) for pop in ts.populations()] ind_node = ts.node(ind.nodes[0]) ind_pop_metadata = pop_metadata[ind_node.population]</code></pre> After this, the ind_pop_metadata variable will contain the population level metadata for individual ID 0. The full data pipeline used to generate these tree sequences and associated metadata is available on <a href="https://github.com/mcveanlab/treeseq-inference/tree/master/human-data">GitHub</a>.

opencc-by-4.0May 2019View details →

Inferring whole-genome histories in large population datasets: inferred tree sequences for Simons Genome Diversity Project

Tree sequences inferred for the SGDP autosomes using <a href="https://tsinfer.readthedocs.io/">tsinfer</a> version 0.1.4 and compressed using <a href="https://tszip.readthedocs.io/en/stable/">tszip</a>. Tree sequences can  be decompressed as follows: <pre><code class="language-bash">$ tsunzip sgdp_chr1.trees.tsz</code></pre> Once decompressed, trees files can be loaded and processed using <a href="https://tskit.readthedocs.io">tskit</a>.  <pre><code class="language-python">import tskit ts = tskit.load("sgdp_chr1.trees") # ts is an instance of tskit.TreeSequence print("Chromosome 1 contains {} trees".format(ts.num_trees))</code></pre> Metadata associated with individuals and populations was derived from the original <a href="https://sharehost.hms.harvard.edu/genetics/reich_lab/sgdp/SGDP_metadata.279public.21signedLetter.samples.txt">source</a> and converted to JSON form. For example, to access individual metadata we can use: <pre><code class="language-python">import tskit import json ts = tskit.load("sgdp_chr1.trees") ind = ts.individual(0) metadata_dict = json.loads(ind.metadata)</code></pre> The metadata_dict variable will now contain all the metadata for the individual with ID 0 as a dictionary. Metadata associated with populations can be found in a similar way. Population IDs are associated with individuals via their constituent nodes. For example, <pre><code class="language-python">pop_metadata = [json.loads(pop.metadata) for pop in ts.populations()] ind_node = ts.node(ind.nodes[0]) ind_pop_metadata = pop_metadata[ind_node.population]</code></pre> After this, the ind_pop_metadata variable will contain the population level metadata for individual ID 0. The full data pipeline used to generate these tree sequences and associated metadata is available on <a href="https://github.com/mcveanlab/treeseq-inference/tree/master/human-data">GitHub</a>.

openCC0Jan 2020View details →

Catalog of GenBank sequence read archive (SRA) entries of 16S and 18S rRNA genes from bacterial and protistan planktonic communities along the Eastern Beaufort Sea coast, North Slope, Alaska, 2011-2013

Microbial communities in the coastal Arctic Ocean experience extreme variability in organic matter and inorganic nutrients driven by seasonal shifts in sea ice extent and freshwater inputs. Lagoons border more than half of the Beaufort Sea coast and provide important habitats for migratory fish and seabirds; yet, little is known about the planktonic food webs supporting these higher trophic levels. To investigate seasonal changes in bacterial and protistan planktonic communities, amplicon sequences of 16S and 18S rRNA genes were generated from samples collected during periods of ice-cover (April), ice break-up (June), and open water (August) from shallow lagoons along the eastern Alaska Beaufort Sea coast from 2011 through 2013. This data package catalogs sequence read archive (SRA) entries available through GenBank BioProject PRJNA530074 at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA530074. This data package is associated with the following publication: Kellogg CTE, McClelland JW, Dunton KH and Crump BC (2019) Strong Seasonality in Arctic Estuarine Microbial Food Webs. Front. Microbiol. 10:2628. doi: 10.3389/fmicb.2019.02628 Environmental variables (physiochemical data from YSI and HOBO data loggers, as well as organic matter analysis and stable isotope data from discrete water samples) associated with this genomic dataset are available from the Arctic Data Center: Kenneth Dunton, Byron Crump, and James McClelland. Physical, chemical, and biological data from lagoons and open coastal waters in the nearshore environment of the eastern Alaska Beaufort Sea, 2011-2013. Arctic Data Center. doi:10.18739/A2DG13. To join the two datasets together, please use the provided site codes (column "site_name" here) and collection dates (column "collection_date" here) in each dataset. Note that the site codes in this package are without hyphens (e.g. JAA) while site codes in the above environmental data package have hyphens (e.g. JA-A). Instead of citing this package which is jus

openCC0Apr 2021View details →

Catalog of GenBank sequence read archive (SRA) entries of metagenomic DNA sequence analyses of bacterial and archaeal water column communities along the Eastern Beaufort Sea coast, North Slope, Alaska, 2012

In contrast to temperate systems, Arctic lagoons that span the Alaska Beaufort Sea coast face extreme seasonality. Nine months of ice cover up to ∼1.7 m thick is followed by a spring thaw that introduces an enormous pulse of freshwater, nutrients, and organic matter into these lagoons over a relatively brief 2–3 week period. Prokaryotic communities link these subsidies to lagoon food webs through nutrient uptake, heterotrophic production, and other biogeochemical processes, but little is known about how the genomic capabilities of these communities respond to seasonal variability. This study characterizes the metabolic capabilities of microbial communities across three seasons in two lagoons and one open coastal site along the eastern Alaska Beaufort Sea coast. We used metagenomic DNA sequence data of bacterial and archaeal water column communities to identify genes of relevant biogeochemical pathways. This data package catalogs sequence read archive (SRA) entries available through GenBank BioProject PRJNA642637 at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA642637. This data package is associated with the following publication: Baker, Kristina D., Colleen T. E. Kellogg, James W. McClelland, Kenneth H. Dunton, and Byron C. Crump. “The Genomic Capabilities of Microbial Communities Track Seasonal Variation in Environmental Conditions of Arctic Lagoons.” Frontiers in Microbiology 12 (2021). https://doi.org/10.3389/fmicb.2021.601901. Environmental variables (physiochemical data from YSI and HOBO data loggers, as well as organic matter analysis and stable isotope data from discrete water samples) associated with this genomic dataset are available from the Arctic Data Center: Kenneth Dunton, Byron Crump, and James McClelland. Physical, chemical, and biological data from lagoons and open coastal waters in the nearshore environment of the eastern Alaska Beaufort Sea, 2011-2013. Arctic Data Center. doi:10.18739/A2DG13. To join the two datasets together, please use the provi

openCC (other)Nov 2014View details →

Cichlasoma urophthalmus cytochrome b sequences collected from the Florida Everglades (FCE) and Central America from January 2012 to May 2014

Cytochrome b sequences obtained from samples of Cichlasoma urophthalmus collected within the Florida Everglades and Central America. Samples were collected from fin clippings of fish caught by angling and/or cast netting. DNA was extracted, amplified and cytochrome b was sequenced. Data were used to determine the source for Mayan Cichlids in Florida.

openCC (other)Dec 2025View details →

16S rRNA gene sequence accessions from discrete water column samples collected from lakes in the McMurdo Dry Valleys, Antarctica (2013-2023, ongoing)

An important component of the McMurdo Dry Valleys Long Term Ecological Research (MCM LTER) project is monitoring spatial and temporal patterns in the biological composition of perennially ice-covered lakes in Antarctica’s McMurdo Dry Valleys. This data package contributes to this core research area by providing a curated table linking 16S rRNA gene sequence accession numbers archived in NCBI to MCM LTER limnological sampling campaigns conducted at specific depths along the water column of Lakes Fryxell, Hoare, Bonney, and Miers. These data enable integration of microbial community data with co-collected biological, chemical, and physical measurements.

opencc-by-4.0Nov 2019View details →

MACREL software benchmark data set: Simulated metagenomes with sequencing quality, errors profile and abundance distributions derived from real samples

These metagenomes were used in the benchmarking of FACS pipeline, and were designed after NGLess benchmark dataset (doi.org/10.5281/zenodo.2560288).  Metagenomes were simulated with <a href="https://www.niehs.nih.gov/research/resources/software/biostatistics/art/index.cfm">ART-bin-MountRainier-2016.06.05</a> using real abundance profiles (.abund files) available <a href="https://doi.org/10.5281/zenodo.2560288">elsewhere</a>, and <a href="http://progenomes1.embl.de/data/repGenomes/representatives.contigs.fasta.gz">proGenomes' representative contigs</a> as reference genomes. There are available metagenomes with 40, 60 and 80 M (million of reads) based in the reference genomes and abundances of the following samples: <pre><code>SAMEA2466916 SAMEA2466953 SAMEA2466965 SAMEA2621107 SAMEA2621229 SAMEA2621247</code></pre> To convert them from the CRAM format back to fastq files: <pre><code> ## 1. converting from cram to bam format: samtools view -b -T refgenome.fa -o file.bam file.cram ## 2. sorting the bam file: samtools sort -n file.bam -o input_sorted.bam # sort reads by identifier-name (-n) ## 3. converting from bam to fastq format: bedtools bamtofastq -i input_sorted.bam -fq output_r1.fastq -fq2 output_r2.fastq </code></pre>

opencc-by-4.0Mar 2020View details →

Cyclist Actions: Optical Flow Sequences and Trajectories

The dataset consists of over 1.1 million samples of labeled cyclists actions. Every sample consists of two optical flow sequences, recorded over the past second (9 optical flow images each), from two different cameras, the past trajectory of the cyclist of the last second (50 past positions), and a label of the currently performed action. The samples were extracted from 1,639 video sequences of cyclists moving across an urban intersection at the University of Applied sciences in Aschaffenburg: <a href="https://www.th-ab.de/ueber-uns/organisation/labor/kooperative-automatisierte-verkehrssysteme/ausstattung/">https://www.th-ab.de/ueber-uns/organisation/labor/kooperative-automatisierte-verkehrssysteme/ausstattung/</a> The uploaded files consist of an archive containing 27 numpy files, a single numpy file containing trajectories only, and a json file containing 5-fold cross validation/test split. The numpy files consist of python dictionaries with scenes of the form: <pre><code class="language-python">{SCENE_NAME: 'of_hk1/2': [...], # zip compressed, python pickled optical flow sequences of cameras 1/2 'x/y/z_tracked': [...], # tracked cyclists positions in x/y/z directions, 'x/y/z_smoothed': [...], # smoothed (by rts smoother) cyclists positions in x/y/z directions, 'orientation': [...], # orientation of the cyclists estimated by kalman filters 'ts': [...], # utc timestamps in micro seconds LABEL_NAME: [...], # labels of different actions (0 or 1)}</code></pre> The manually created labels are: <ul> <li>straight: cyclists is moving and not turning</li> <li>tr/tl: cyclist is turning left/right</li> <li>move: cyclist is moving with nearly constant velocity and not turning</li> <li>start: cyclist was standing and starts moving</li> <li>starting_movement: first movement of cyclist before starting</li> <li>stop: cyclist was moving/starting and slows down to a halt</li> <li>wait: cyclist is standing</li> <li>hand_signal_left/right: cyclist indicates a turn by hand signal</li> <li>shoulder_check_left/right: cyclist looks over left/right shoulder</li> <li>out_of_saddle: cyclist is standing</li> </ul> The optical flow sequences were created using PWC-Net [1]. To extract the zipped/pickled optical flow sequences: <pre><code class="language-python">import cv2 as cv import zlib import pickle import numpy as np # visualize flow def vis_of(of): hsv = np.zeros([of.shape[0], of.shape[1], 3], dtype=np.uint8) hsv[..., 1] = 255 mag, ang = cv.cartToPolar(of[..., 0].astype(np.float32), of[..., 1].astype(np.float32)) hsv[..., 0] = ang * 180 / np.pi / 2 hsv[..., 2] = cv.normalize(mag, None, 0, 255, cv.NORM_MINMAX) bgr = cv.cvtColor(hsv, cv.COLOR_HSV2BGR) return bgr # load npy file from dataset npy_path = 'of_dataset_0.npy' data = np.load(npy_path, allow_pickle=True).item() scene = data[list(data.keys())[0]] # extract optical flow sequence ofs = pickle.loads(zlib.decompress(scene['of_hk1'][i])).astype(np.float16) * 2.0 / 255.0 - 1.0 # show of images in sequence for j in range(len(ofs)): # create bgr image from 2 channel optical flow bgr = vis_of(ofs[j]) cv.imshow("of", bgr) </code></pre> Python code and a description to read the dataset can be found in our GitHub: <a href="https://github.com/CooperativeAutomatedTrafficSystemsLab/CyclistActionRecognition">https://github.com/CooperativeAutomatedTrafficSystemsLab/CyclistActionRecognition</a> [1] D. Sun, X. Yang, M. Liu, and J. Kautz, “PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, June 2018, pp. 8934–8943.   This work results from the project DeCoInt 2, supported by the German Research Foundation (DFG) within the priority program SPP 1835: "Kooperativ interagierende Automobile", grant numbers DO 1186/1-2, FU 1005/1-2, and SI 674/11-2. Additionally, the work is supported by "Zentrum Digitalisierung Bayern". Due to privacy laws in germany, we are not permitted to publish image sequences.

opencc-by-4.0Apr 2020View details →

BAMBI ITS - Analysis of the fungal component (via ITS amplicon sequencing) of stool samples from preterm babies

Amplicon analysis of ITS amplicons from preterm babies. Associated GitHub repository: <a href="https://github.com/quadram-institute-bioscience/bambi-its">https://github.com/quadram-institute-bioscience/bambi-its</a>

opencc-by-4.0Apr 2020View details →

Genome sequence of the banana aphid, Pentalonia nigronervosa Coquerel (Hemiptera: Aphididae) and its symbionts

Pentalonia nigronervosa v1 frozen release Genome assembly: Pentalonia_nigronervosa.v1.scaffolds.fa.gz BRAKER2 gene models: Pentalonia_nigronervosa.v1.scaffolds.gff BRAKER2 protein sequences: Pentalonia_nigronervosa.v1.scaffolds.gff.aa.fa BRAKER2 protein sequences (longest transcript per gene only): Pentalonia_nigronervosa.v1.scaffolds.gff.aa.LTPG.fa BRAKER2 coding sequences: Pentalonia_nigronervosa.v1.scaffolds.gff.cds.fa InterProScan functional annotation: Pentalonia_nigronervosa.v1.scaffolds.gff.aa.LTPG.interproscan.tsv Pentalonia nigronervosa v1 mitochondrial genome: Pentalonia_nigronervosa.v1.mt_genome.fa Buchnera aphidicola (BPn) scaffolds: Buchnera_aphidicola_BPn.scaffolds.fa Wolbachia (WolPenNig) scaffolds: Wolbachia_WolPenNig.scaffolds.fa Myzus cerasi v1.2 frozen release Genome assembly: Myzus_cerasi.v1.2.scaffolds.fa BRAKER2 gene models: Myzus_cerasi.v1.2.scaffolds.gff BRAKER2 protein sequences: Myzus_cerasi.v1.2.scaffolds.gff.aa.fa BRAKER2 protein sequences (longest transcript per gene only): Myzus_cerasi.v1.2.scaffolds.gff.aa.LTPG.fa BRAKER2 coding sequences: Myzus_cerasi.v1.2.scaffolds.gff.cds.fa Aphid orthogroups and species tree Proteomes included in the analysis: proteomes.tar.gz Orthogroups: Orthogroups.txt Gene counts per orthogroup, per species: Orthogroups.GeneCount.csv Single copy conserved orthogroups used for species tree: Orthogroups_for_concatenated_alignment.txt Species tree alignment: SpeciesTreeAlignment.fa Rooted species tree: SpeciesTree_rooted.nwk Bash script to run k-mer based assembly deduplication pipeline File: disco_filter_dups.v1.1.sh This script will parse a discovar de novo assembly and remove scaffolds likely to be haplotigs based on their k-mer content and a self alignment of the assembly (see manuscript for details). The input discovar assembly needs to have white space in scaffold IDs replaced with "_" before running. Illumina reads should be unzipped before running. Usage: <pre><code class="language-bash">sh disco_filter_dups.sh <./path_to_assembly> <./path_to_r1> <./path_to_r2> <homozyzgous_lower_cov> <homozyzgous_upper_cov> <nucmer_id_cutoff> <nucmer_cov_cutoff> <assembly_output_prefix> <threads> <./working_dir></code></pre>

opencc-by-4.0Apr 2020View details →

COG-UK Viral Genome Sequences

COG-UK Consortium has published dataset contains over 10K SARS-CoV-2 viral genome sequences available as open access. The current COVID-19 pandemic, caused by the SARS-CoV-2 virus, represents a major threat to health in the UK and globally. To fully understand the transmission and evolution of the virus requires sequencing and analysing viral genomes at scale and speed. The numbers of samples calls for a rapid increase in the UK’s pathogen genome sequencing capacity rapidly and robustly. To provide this increased capacity to collect, sequence and analyse the whole genomes of virus samples in the UK, the COVID-19 Genomics UK (COG-UK) consortium is pooling the world-leading knowledge and expertise in genomics of the four UK Public Health Agencies, multiple regional University hubs, and large sequencing centres such as the Wellcome Sanger Institute. <ul> <li>Protocols: https://www.cogconsortium.uk/protocols/</li> </ul>

opencc-by-4.0May 2020View details →

Raw read counts and phased SNP counts for every single cell in the sequencing datasets of the breast cancer patient S1 from "Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL"

This dataset contains the raw read counts and phased SNP counts for every single cell in the sequencing datasets of breast cancer patient S1 from “Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL” [Zaccaria & Raphael, 2020]. These data enable the full reproduction of all the results in the related manuscript for breast cancer patient S1. Specifically, the data are provided in two files for every dataset DAT of patient S1 with the following format: <ol> <li>DAT.raw_read_counts.bed.gz is a multi-cell BED file containing the raw read counts in the following fields: <ul> <li>CHROMOSOME: the name of a human chromosome</li> <li>START: the starting genomic position of a genomic bin in the chromosome</li> <li>END: the ending genomic position of the genomic bin in the chromosome</li> <li>CELL: the cell barcode that uniquely identifies a cell</li> <li>NORMAL: the raw read count for the specified bin from a matched-normal sample</li> <li>COUNT: the raw read count for the specified bin in the specified cell</li> <li>RDR: the estimated read-depth ratio for the specified bin in the specified cell</li> </ul> </li> <li>DAT.phased_snps_counts.pos.gz is a multi-cell POS file containing the phased SNP counts in the following fields: <ul> <li>CHROMOSOME: the name of a human chromosome</li> <li>POS: the genomic position in the chromosome of a germline SNP</li> <li>CELL: the cell barcode that uniquely identifies a cell</li> <li>COUNT_HAPLOTYPE_A: the count of reads that cover the SNP and that belong to haplotype A in the specified cell</li> <li>COUNT_HAPLOTYPE_B: the count of reads that cover the SNP and that belong to haplotype B in the specified cell</li> </ul> </li> </ol> All the files have been compressed using standard gzip.

opencc-by-4.0May 2020View details →

Raw read counts and phased SNP counts for every single cell in the sequencing datasets of the breast cancer patient S0 from "Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL"

This dataset contains the raw read counts and phased SNP counts for every single cell in the sequencing datasets of breast cancer patient S0 from “Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL” [Zaccaria & Raphael, 2020]. These data enable the full reproduction of all the results in the related manuscript for breast cancer patient S0. Specifically, the data are provided in two files for every dataset DAT of patient S0 with the following format: <ol> <li>DAT.raw_read_counts.bed.gz is a multi-cell BED file containing the raw read counts in the following fields: <ul> <li>CHROMOSOME: the name of a human chromosome</li> <li>START: the starting genomic position of a genomic bin in the chromosome</li> <li>END: the ending genomic position of the genomic bin in the chromosome</li> <li>CELL: the cell barcode that uniquely identifies a cell</li> <li>NORMAL: the raw read count for the specified bin from a matched-normal sample</li> <li>COUNT: the raw read count for the specified bin in the specified cell</li> <li>RDR: the estimated read-depth ratio for the specified bin in the specified cell</li> </ul> </li> <li>DAT.phased_snps_counts.pos.gz is a multi-cell POS file containing the phased SNP counts in the following fields: <ul> <li>CHROMOSOME: the name of a human chromosome</li> <li>POS: the genomic position in the chromosome of a germline SNP</li> <li>CELL: the cell barcode that uniquely identifies a cell</li> <li>COUNT_HAPLOTYPE_A: the count of reads that cover the SNP and that belong to haplotype A in the specified cell</li> <li>COUNT_HAPLOTYPE_B: the count of reads that cover the SNP and that belong to haplotype B in the specified cell</li> </ul> </li> </ol> All the files have been compressed using standard gzip.

opencc-by-4.0May 2020View details →

TAASRAD19 Radar Sequences 2010-2019 HDF5

TAASRAD19 (Trentino-Alto Adige/Südtirol Radar 2019) is a high-resolution radar reflectivity dataset collected by the Civil Protection weather radar of the Trentino South Tyrol Region, in the Italian Alps. The dataset includes 894,916 scans of precipitation from more than 9 years of data, offering a novel resource to develop and benchmark analog ensemble models and machine learning solutions for precipitation nowcasting. Data are expressed as 2D images, considering the maximum reflectivity on the vertical section and 5 minutes sampling rate, covering an area of 240km of diameter at 500m horizontal resolution. The TAASRAD19 distribution also includes a curated set of 1,732 sequences, for a total of 362,233 radar images, labeled with precipitation type tags assigned by expert meteorologists. We validated TAASRAD19 as a benchmark for nowcasting using deep learning model to forecast reflectivity and a procedure based on the UMAP dimensionality reduction method for interactive exploration. Software methods for data pre-processing, model training and inference, and a pre-trained model are publicly available at https://github.com/MPBA/TAASRAD19 for replication and reproducibility.