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Dataset results
14 results for “2009-2013”
Ecosystem-scale rainfall manipulation in a Pinon-Juniper Woodland: Volumetric Water Content (VWC) Profile Data (2009-2013 )
Climate models predict that water limited regions around the world will become drier and warmer in the near future, including southwestern North America. We developed a large-scale experimental system that allows testing of the ecosystem impacts of precipitation changes. Four treatments were applied to 1600 m2 plots (40 m × 40 m), each with three replicates in a piñon pine (Pinus edulis) and juniper (Juniper monosperma) ecosystem. These species have extensive root systems, requiring large-scale manipulation to effectively alter soil water availability. Treatments consisted of: 1) irrigation plots that receive supplemental water additions, 2) drought plots that receive 55% of ambient rainfall, 3) cover-control plots that receive ambient precipitation, but allow determination of treatment infrastructure artifacts, and 4) ambient control plots. Our drought structures effectively reduced soil water potential and volumetric water content compared to the ambient, cover-control, and water addition plots. Drought and cover control plots experienced an average increase in maximum soil and air temperature at ground level of 1-4° C during the growing season compared to ambient plots, and concurrent short-term diurnal increases in maximum air temperature were also observed directly above and below plastic structures. Our drought and irrigation treatments significantly influenced tree predawn water potential, sap-flow, and net photosynthesis, with drought treatment trees exhibiting significant decreases in physiological function compared to ambient and irrigated trees. Supplemental irrigation resulted in a significant increase in both plant water potential and xylem sap-flow compared to trees in the other treatments. This experimental design effectively allows manipulation of plant water stress at the ecosystem scale, permits a wide range of drought conditions, and provides prolonged drought conditions comparable to historical droughts in the past – drought events for which wide
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>
rDNA 18S V9 metabarcoding tables (Swarm) for Tara Oceans Expedition (2009-2013), including Tara Polar Circle Expedition (2013)
<p>Reads were grouped into OTUs using the following swarm-based pipeline: paired-end reads were merged with vsearch’s --fastq_mergepairs command (version 2.15.1, allowing for staggered reads; Rognes et al., 2016), and trimmed with cutadapt (version 3.0; Martin, 2011), keeping only reads containing both forward and reverse primers. After trimming, the expected error per read was estimated with vsearch’s command --fastq_filter and the option --eeout. Each sample was then de-replicated, i.e. strictly identical reads were merged, using vsearch’s command --derep_fulllength, and converted into fasta format. Clustering was performed at the sample level with swarm 3.0 using default parameters (Mahé et al., 2015). Prior to global clustering, individual fasta files (one per sample) were pooled and further dereplicated with vsearch. Files containing per-read expected error values were also dereplicated to retain only the lowest expected error for each unique sequence. Global clustering was performed with swarm (using the fastidious option). Cluster representative sequences were then searched for chimeras with vsearch’s command --uchime_denovo using default parameters (Edgar et al., 2011).</p> <p>Clustering results, expected error values, taxonomic assignments, and chimera detection results were used to build a “raw” occurrence table. Reads without primers, reads shorter than 32 nucleotides and reads with uncalled bases (“N”) were discarded. For a “filtered” occurrence table, non-chimeric sequences, sequences with an expected error per nucleotide below 0.0002, and clusters containing at least 2 reads were retained. Since primer trimming is not perfect, some sequences can still contain primer fragments or be excessively trimmed. These sub- or super-sequences were identified using vsearch and merged with their closest, most abundant perfectly trimmed sequence. Finally, occurrence patterns throughout our sample collection were used to further refine the occurrence table. Clusters that contain sub-clusters with only a single-nucleotide difference but with different ecological patterns (defined here as uncorrelated abundance values in at least 5% of the samples) were turned into distinct clusters (https://github.com/frederic-mahe/fred-metabarcoding-pipeline). On the other hand, clusters with similar sequences that had correlated abundance values in at least 95% of the samples, were merged using a re-implementation of lulu's method (Frøslev et al. 2017; https://github.com/frederic-mahe/mumu).</p> <p> </p>
Tara Oceans (2009-2013) rDNA 18S V9 ASV table (DADA2) with nf-core/ampliseq
<p>This repository contains datasets describing the DADA2 ASVs generated from <em>Tara</em> Oceans 18S V9 rDNA data. The ASVs were generated using the nf-core workflow <a href="https://nf-co.re/ampliseq" target="_blank" rel="noopener">ampliseq</a>. Please refer to the readme file (README.html) for more details.</p>
Tara Oceans (2009-2013) rDNA 18S V4 ASV table (DADA2) with nf-core/ampliseq
<p>This repository contains datasets describing the DADA2 ASVs generated from <em>Tara</em> Oceans 18S V4 rDNA data. The ASVs were generated using the nf-core workflow <a href="https://nf-co.re/ampliseq" target="_blank" rel="noopener">ampliseq</a>. Please refer to the readme file (README.html) for more details.</p>
Eight Mile Lake Research Watershed, Carbon in Permafrost Experimental Heating Research (CiPEHR): Fall ecosystem respiration chamber measurements, 2009; 2011-2013.
The Carbon in Permafrost Experimental Heating Research (CiPEHR) project addresses the following questions: 1) Does ecosystem warming cause a net release of C from the ecosystem to the atmosphere?, 2) Does the decomposition of old C, that comprises the bulk of the soil C pool, influence ecosystem C loss?, and 3) How do winter and summer warming alone, and in combination, affect ecosystem C exchange? We are answering these questions using a combination of field and laboratory experiments to measure ecosystem carbon balance and radiocarbon isotope ratios at a warming experiment located in an upland tundra field site near Healy, Alaska in the foothills of the Alaska Range. This dataset contains static chamber measurements of CO2 flux at CiPEHR during the falls of 2009,2011, 2012, and 2013.
rDNA 18S V4 metabarcoding tables (Swarm) for Tara Oceans Expedition (2009-2013), including Tara Polar Circle Expedition (2013)
<p>Reads were grouped into OTUs using the following swarm-based pipeline: paired-end reads were merged with vsearch’s --fastq_mergepairs command (version 2.15.1, allowing for staggered reads; Rognes et al., 2016), and trimmed with cutadapt (version 3.0; Martin, 2011), keeping only reads containing both forward and reverse primers. After trimming, the expected error per read was estimated with vsearch’s command --fastq_filter and the option --eeout. Each sample was then de-replicated, i.e. strictly identical reads were merged, using vsearch’s command --derep_fulllength, and converted into fasta format. Clustering was performed at the sample level with swarm 3.0 using default parameters (Mahé et al., 2015). Prior to global clustering, individual fasta files (one per sample) were pooled and further dereplicated with vsearch. Files containing per-read expected error values were also dereplicated to retain only the lowest expected error for each unique sequence. Global clustering was performed with swarm (using the fastidious option). Cluster representative sequences were then searched for chimeras with vsearch’s command --uchime_denovo using default parameters (Edgar et al., 2011).<br> Clustering results, expected error values, taxonomic assignments, and chimera detection results were used to build a “raw” occurrence table. Reads without primers, reads shorter than 32 nucleotides and reads with uncalled bases (“N”) were discarded. For a “filtered” occurrence table, non-chimeric sequences, sequences with an expected error per nucleotide below 0.0002, and clusters containing at least 2 reads were retained. Since primer trimming is not perfect, some sequences can still contain primer fragments or be excessively trimmed. These sub- or super-sequences were identified using vsearch and merged with their closest, most abundant perfectly trimmed sequence. Finally, occurrence patterns throughout our sample collection were used to further refine the occurrence table. Clusters that contain sub-clusters with only a single-nucleotide difference but with different ecological patterns (defined here as uncorrelated abundance values in at least 5% of the samples) were turned into distinct clusters (https://github.com/frederic-mahe/fred-metabarcoding-pipeline). On the other hand, clusters with similar sequences that had correlated abundance values in at least 95% of the samples, were merged using a re-implementation of lulu's method (Frøslev et al. 2017; https://github.com/frederic-mahe/mumu).</p>
The Eurosprite 2009-2013 Catalogue
<p>The Eurosprite 2009-2013 Catalogue contains the summary of optical observations of transient luminous events (TLEs) collected by the Eurosprite observational network and partners over Europe and the Mediterranean Sea during 2009 to 2013. Each entry in the catalogue consists of a summary of TLEs recorded by the observer during a thunderstorm/observing period (e.g., the active period of a thunderstorm or more cells together) and reports the time span and approximate geographical area of the observations, the number and types of TLEs that were recorded, a reference person and additional comments. Optical images related to each entry of the catalogue are stored and maintained by the observer and can be reached through its contact person. A detailed description of the observational network, of the observations and on the interpretation of the observations can be found in Arnone et al., 2019, Surveys in Geophysics.</p> <p>Each entry is reported in the following terms.</p> <ul> <li>DATE: date of start of sample (only start even if crossing the night)</li> <li>UT: UT time of start and end of sample (end can be in the next day)</li> <li>LON: approximate Lon1/Lon2 range of the area affected by TLEs</li> <li>LAT: approximate Lat1/Lat2 range of the area affected by TLEs</li> <li>TLEs: total number of TLEs observed (with the number of individual types of TLEs observed, i.e. sp: sprites, el: elves, ha: halos, bj: blue jets, gj: gigantic jets, ot: other types). Clearly distinguished TLEs in the same frame count as separate ones. TLEs that appear part of the same event count as one. For this reason the total number of TLEs may be smaller than the sum of individual types.</li> <li>REF: a contact person for the entry</li> <li>COM: additional comments on e.g., cameras involved or partners</li> </ul>
Permafrost soil database with information on site, topography, geomorphology, hydrology, soil stratigraphy, soil carbon, ground ice isotopes, and vegetation at thermokarst features near Toolik and Noatak River, 2009-2013
This database contains soil and permafrost stratigraphy associated with thermokarst features near Toolik Lake and the Noatak River collected by Torre Jorgenson and Andrew Balser during summers 2009-2011. The Access Database has main data tables (tbl_) for site (environmental), soil stratigraphy, soil physical data, soil chemical data, soil isotopes (ground ice), soil radiocarbon dates, topography and bathymetry, and vegetation cover. The site data includes information of location, observers, geomorphology, topography, hydrology, soil summary characteristics, pH and EC, soil classification, and vegetation cover by species. Soil stratigrapy has information on soil texture and ground ice. Soil physical and chemical data includes lab data on bulk density, moisture, carbon, and nitrogen. The database has 37 reference tables (REF_) that have codes and descriptions for variables used in site, soil stratigraphy, and vegetation cover tables.
Dataset to train, validate and reconstruct POC over the global ocean for 2009-2013 based on PlankTOM12
<p>The distribution of in situ UVP5 measurements over the period 2009-2013 was used to create synthetic data by sampling a global biogeochemical ocean model PlankTOM12 at UVP5 time and location.</p> <p>The synthetic data set is used to train, validate and test Machine Learning methods to reconstruct particulate organic carbon concentration. </p> <p>These data are part of publication at the GMD. </p>
CARVE: Monthly Atmospheric CO2 Concentrations (2009-2013) and Modeled Fluxes, Alaska
This data set reports monthly averages of atmospheric CO2 concentration from satellite and airborne observations between 2009 and 2013 and simulated present and future monthly concentrations and land-atmosphere CO2 flux for periods between 1990 and 2200. Atmospheric CO2 concentration measurements were obtained from Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) and NOAA Arctic Coast Guard (ACG) flights, the Greenhouse Gases Observing Satellite (GOSAT), and NOAA/ESRL vertical profile measurements at Poker Flat, Alaska (PFA). Present and future monthly CO2 concentrations and fluxes were simulated using the GEOS-Chem global tracer model and the Community Land Model, Version 4.5, for multiple regional flux and permafrost thaw scenarios.
A Retrospective Chart Review of Thirty Three Children Who Have Received Clinical Treatment With Either Romiplostim or Eltrombopag From 2009-2013
ClinicalTrials.gov study NCT01974232. IPD Sharing: NO. Countries: 1. Publications: 0.
Single cell rDNA 16S V4V5 and 18S V9 metabarcoding tables (dada2) from the Tara Oceans expedition (2009-2013)
<p>Single cell rDNA 16S V4V5 and 18S V9 metabarcoding tables (dada2) from the Tara Oceans expedition (2009-2013). The code to generate the ASV tables is here: https://gitlab.com/tara-and-friends-euk-metab/tara-single-cell-metab</p>
Tara Oceans (2009-2013) rDNA 16S V4V5 ASV table (dada2)
<p>This repository contains a rDNA 16S V4V5 ASV table (TARA-Oceans_16S-V4V5_dada2_table.tsv.gz) for <em>Tara</em> Oceans (2009-2013).</p> <p>In the file <strong>TARA-Oceans_16S-V4V5_dada2_table.tsv.gz</strong>, each ASV, one per row, is described by the following fields: <strong>amplicon</strong> = ASV identifier; <strong>taxonomy</strong> = taxonomic path assigned to the ASV using IDTAXA; <strong>confidence</strong> = IDTAXA confidence scores for each taxonomic rank; <strong>total</strong> = total number of reads for the entire dataset; <strong>spread</strong> = number of samples in which the ASV is detected; <strong>sequence</strong> = ASV nucleic acid sequence; <strong>TARA_XXXXXXXXXX</strong> = number of reads in each of the 1,134 <em>Tara</em> Oceans samples.</p> <p>Detailed information about DNA extraction, PCR amplification and Illumina sequencing of metabarcodes, as well as subsequent sequence data cleaning and taxonomic assignment can be found in (Alberti et al. 2017). Briefly, DNA samples were amplified by PCR targeting the hypervariable region V4V5 (primer pair: 15F-Y 5′- GTGYCAGCMGCCGCGGTAA-3′ and 926R 5′- CCGYCAATTYMTTTRAGTTT-3′ 16S primers; (Parada et al. 2016)) of the 16S rRNA marker gene followed by the high-throughput sequencing of the amplicons. The details of PCR mixes, thermocycling and sequencing conditions are provided in Alberti et al. (2017).</p> <p>Resulting paired-end reads were mixed-oriented meaning that both R1 and R2 files are composed by a mix of forward and reverse reads. Paired-end reads were trimmed to remove PCR primer sequences using Cutadapt v2.7 (Martin, 2011) and dispatched into four files, 2 files for the classical orientation (forward reads in R1 and reverse reads in R2) and 2 others for the other orientation (reverse reads in R1 and forward reads in R2). Paired-end reads without both primers were filtered out using the option --discard-untrimmed. Forward and reverse reads were trimmed at position 215 and 190 for R1 and R2 files respectively. Reads with ambiguous nucleotides or with a maximum number of expected errors (maxEE) superior to 2 were filtered out using the function filterAndTrim() from the R package dada2 (Callahan et al., 2016). For each run and read orientation, error rates were defined using the function learnErrors() and denoised using the dada() function with pool = TRUE before being merged using mergePairs() with default parameters. Mixed orientated reads from the same sample and sequencing replicates were summed together. Remaining chimeras were removed using the function removeBimeraDenovo(). Scripts producing the ASV table are publicly available here: https://gitlab.univ-nantes.fr/combi-ls2n/taradada.</p> <p>ASVs were taxonomically assigned using IDTAXA (50% confidence threshold) (Murali, Bhargava, and Wright 2018) with SILVA v138.</p> <p>Alberti, A., Poulain, J., Engelen, S., Labadie, K., Romac, S., Ferrera, I., Albini, G., Aury, J.-M., Belser, C., Bertrand, A., Cruaud, C., Da Silva, C., Dossat, C., Gavory, F., Gas, S., Guy, J., Haquelle, M., Jacoby, E., Jaillon, O., Lemainque, A., Pelletier, E., Samson, G., Wessner, M., Genoscope Technical Team, Bazire, P., Beluche, O., Bertrand, L., Besnard-Gonnet, M., Bordelais, I., Boutard, M., Dubois, M., Dumont, C., Ettedgui, E., Fernandez, P., Garcia, E., Aiach, N.G., Guerin, T., Hamon, C., Brun, E., Lebled, S., Lenoble, P., Louesse, C., Mahieu, E., Mairey, B., Martins, N., Megret, C., Milani, C., Muanga, J., Orvain, C., Payen, E., Perroud, P., Petit, E., Robert, D., Ronsin, M., Vacherie, B., Acinas, S.G., Royo-Llonch, M., Cornejo-Castillo, F.M., Logares, R., Fernández-Gómez, B., Bowler, C., Cochrane, G., Amid, C., Hoopen, P.T., De Vargas, C., Grimsley, N., Desgranges, E., Kandels-Lewis, S., Ogata, H., Poulton, N., Sieracki, M.E., Stepanauskas, R., Sullivan, M.B., Brum, J.R., Duhaime, M.B., Poulos, B.T., Hurwitz, B.L., Tara Oceans Consortium Coordinators, Acinas, S.G., Bork, P., Boss, E., Bowler, C., De Vargas, C., Follows, M., Gorsky, G., Grimsley, N., Hingamp, P., Iudicone, D., Jaillon, O., Kandels-Lewis, S., Karp-Boss, L., Karsenti, E., Not, F., Ogata, H., Pesant, S., Raes, J., Sardet, C., Sieracki, M.E., Speich, S., Stemmann, L., Sullivan, M.B., Sunagawa, S., Wincker, P., Pesant, S., Karsenti, E., Wincker, P., 2017. Viral to metazoan marine plankton nucleotide sequences from the Tara Oceans expedition. Sci Data 4, 170093. <a href="https://doi.org/10.1038/sdata.2017.93">https://doi.org/10.1038/sdata.2017.93</a></p> <p>Callahan, B.J., McMurdie, P.J., Rosen, M.J., Han, A.W., Johnson, A.J.A., Holmes, S.P., 2016. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods 13, 581–583. <a href="https://doi.org/10.1038/nmeth.3869">https://doi.org/10.1038/nmeth.3869</a></p> <p>Martin, M., 2011. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet j. 17, 10. <a href="https://doi.org/10.14806/ej.17.1.200">https://doi.org/10.14806/ej.17.1.200</a></p> <p>Murali, A., Bhargava, A., Wright, E.S., 2018. IDTAXA: a novel approach for accurate taxonomic classification of microbiome sequences. Microbiome 6, 140. <a href="https://doi.org/10.1186/s40168-018-0521-5">https://doi.org/10.1186/s40168-018-0521-5</a></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
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.