A first estimate of retail sales in value and volume terms for Great Britain, seasonally and non-seasonally adjusted.

Load, wind and solar, prices in hourly resolution. This data package contains different kinds of timeseries data relevant for power system modelling, namely electricity prices, electricity consumption (load) as well as wind and solar power generation and capacities. The data is aggregated either by country, control area or bidding zone. Geographical coverage includes the EU and some neighbouring countries. All variables are provided in hourly resolution. Where original data is available in higher resolution (half-hourly or quarter-hourly), it is provided in separate files. This package version only contains data provided by TSOs and power exchanges via ENTSO-E Transparency, covering the period 2015-mid 2020. See previous versions for historical data from a broader range of sources. All data processing is conducted in Python/pandas and has been documented in the Jupyter notebooks linked below.

Baltic Dry fell to 2,203 Index Points on September 19, 2025, down 0.09% from the previous day. Over the past month, Baltic Dry's price has risen 14.32%, and is up 11.43% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Baltic Exchange Dry Index - values, historical data, forecasts and news - updated on September of 2025.

The "https://electionstudies.org/data-center/2020-time-series-study/" Target="_blank">American National Election Studies (ANES) 2020 Time Series Study is a continuation of the series of election studies conducted since 1948 to support analysis of public opinion and voting behavior in U.S. presidential elections. This year's study features re-interviews with "https://electionstudies.org/data-center/2016-time-series-study/" Target="_blank">2016 ANES respondents, a freshly drawn cross-sectional sample, and post-election surveys with respondents from the "https://gss.norc.org/" Target="_blank">General Social Survey (GSS). All respondents were assigned to interview by one of three mode groups - by web, video or telephone. The study has a total of 8,280 pre-election interviews and 7,449 post-election re-interviews.

New content for the 2020 pre-election survey includes variables on sexual harassment and misconduct, health insurance, identity politics, immigration, media trust and misinformation, institutional legitimacy, campaigns, party images, trade tariffs and tax policy.

New content for the 2020 post-election survey includes voting experiences, attitudes toward public health officials and organizations, anti-elitism, faith in experts/science, climate change, gun control, opioids, rural-urban identity, international trade, sexual harassment and #MeToo, transgender military service, perception of foreign countries, group empathy, social media usage, misinformation and personal experiences.

(American National Election Studies. 2021. ANES 2020 Time Series Study Full Release [dataset and documentation]. July 19, 2021 version. "https://electionstudies.org/" Target="_blank">https://electionstudies.org/)

This data set provides snowmelt timing maps (STMs), cloud interference maps, and a map with the count of calculated snowmelt timing values for North America. The STMs are based on the Moderate Resolution Imaging Spectroradiometer (MODIS) standard 8-day composite snow-cover product MOD10A2 collection 6 for the period 2001-01-01 to 2018-12-31. The STMs were created by conducting a time-series analysis of the MOD10A2 snow maps to identify the DOY of snowmelt on a per-pixel basis. Snowmelt timing (no-snow) was defined as a snow-free reading following two consecutive snow-present readings for a given 500-m pixel. The count of STM values is also reported, which represents the number of years on record in the STMs from 2001-2018.

Graph and download economic data for Personal Consumption Expenditures: Chain-type Price Index (PCEPI) from Jan 1959 to Jul 2025 about chained, headline figure, PCE, consumption expenditures, consumption, personal, inflation, price index, indexes, price, and USA.

CBOE Volatility Index (VIX) time-series dataset including daily open, close, high and low. The CBOE Volatility Index (VIX) is a key measure of market expectations of near-term volatility conveyed by...

International Journal of Computational Intelligence Systems Abstract & Indexing - ResearchHelpDesk - The International Journal of Computational Intelligence Systems is an international peer reviewed journal and the official publication of the European Society for Fuzzy Logic and Technologies (EUSFLAT). The journal publishes original research on all aspects of applied computational intelligence, especially targeting papers demonstrating the use of techniques and methods originating from computational intelligence theory. This is an open access journal, i.e. all articles are immediately and permanently free to read, download, copy & distribute. The journal is published under the CC BY-NC 4.0 user license which defines the permitted 3rd-party reuse of its articles. Aims & Scope The International Journal of Computational Intelligence Systems publishes original research on all aspects of applied computational intelligence, especially targeting papers demonstrating the use of techniques and methods originating from computational intelligence theory. The core theories of computational intelligence are fuzzy logic, neural networks, evolutionary computation and probabilistic reasoning. The journal publishes only articles related to the use of computational intelligence and broadly covers the following topics: Autonomous reasoning Bio-informatics Cloud computing Condition monitoring Data science Data mining Data visualization Decision support systems Fault diagnosis Intelligent information retrieval Human-machine interaction and interfaces Image processing Internet and networks Noise analysis Pattern recognition Prediction systems Power (nuclear) safety systems Process and system control Real-time systems Risk analysis and safety-related issues Robotics Signal and image processing IoT and smart environments Systems integration System control System modelling and optimization Telecommunications Time series prediction Warning systems Virtual reality Web intelligence Deep learning

Graph and download economic data for Nominal Broad U.S. Dollar Index (DTWEXBGS) from 2006-01-02 to 2025-09-12 about trade-weighted, broad, exchange rate, currency, services, goods, rate, indexes, and USA.

Recommended citation

Gütschow, J.; Busch, D.; Pflüger, M. (2024): The PRIMAP-hist national historical emissions time series v2.6 (1750-2023). zenodo. doi:10.5281/zenodo.13752654.

Gütschow, J.; Jeffery, L.; Gieseke, R.; Gebel, R.; Stevens, D.; Krapp, M.; Rocha, M. (2016): The PRIMAP-hist national historical emissions time series, Earth Syst. Sci. Data, 8, 571-603, doi:10.5194/essd-8-571-2016

Content

• Use of the dataset and full description
• Abstract
• Support
• Sources
• Files included in the dataset
• Notes
• Data format description (columns)
• References
• Changelog

Abstract

The PRIMAP-hist dataset combines several published datasets to create a comprehensive set of greenhouse gas emission pathways for every country and Kyoto gas, covering the years 1750 to 2023, and almost all UNFCCC (United Nations Framework Convention on Climate Change) member states as well as most non-UNFCCC territories. The data resolves the main IPCC (Intergovernmental Panel on Climate Change) 2006 categories. For CO2, CH4, and N2O subsector data for Energy, Industrial Processes and Product Use (IPPU), and Agriculture are available. The "country reported data priority" (CR) scenario of the PRIMAP-hist datset prioritizes data that individual countries report to the UNFCCC. For developed countries, AnnexI in terms of the UNFCCC, this is the data submitted anually in the "common reporting format" (CRF). For developing countries, non-AnnexI in terms of the UNFCCC, this is the data available through the UNFCCC DI portal (di.unfccc.int) with additional country submissions read from pdf and where available xls(x) or csv files. For a list of these submissions please see below. For South Korea the 2023 official GHG inventory has not yet been submitted to the UNFCCC but is included in PRIMAP-hist. PRIMAP-hist also includes official data for Taiwan which is not recognized as a party to the UNFCCC.

Gaps in the country reported data are filled using third party data such as CDIAC, EI (fossil CO2), Andrew cement emissions data (cement), FAOSTAT (agriculture), and EDGAR v8.0 (all sectors for CO2, CH4, N2O, except energy CO2), and EDGAR v7.0 (IPPU, f-gases). Lower priority data are harmonized to higher priority data in the gap-filling process.

For the third party priority time series gaps in the third party data are filled from country reported data sources.

Data for earlier years which are not available in the above mentioned sources are sourced from EDGAR-HYDE, CEDS, and RCP (N2O only) historical emissions.

The v2.4 release of PRIMAP-hist reduced the time-lag from 2 to 1 years for the October release. Thus the present version 2.6 includes data for 2023. For energy CO2 growth rates from the EI Statistical Review of World Energy are used to extend the country reported (CR) or CDIAC (TP) data to 2023. For CO2 from cement production Andrew cement data are used. For other gases and sectors we have to rely on numerical methods to estimate emissions for 2023.

Version 2.6 of the PRIMAP-hist dataset does not include emissions from Land Use, Land-Use Change, and Forestry (LULUCF) in the main file. LULUCF data are included in the file with increased number of significant digits and have to be used with care as they are constructed from different sources using different methodologies and are not harmonized.

The PRIMAP-hist v2.6 dataset is an updated version of

Gütschow, J.; Pflüger, M.; Busch, D. (2024): The PRIMAP-hist national historical emissions time series v2.5.1 (1750-2022). zenodo. doi:10.5281/zenodo.10705513.

The Changelog indicates the most important changes. You can also check the issue tracker on github.com/JGuetschow/PRIMAP-hist for additional information on issues found after the release of the dataset. Detailed per country information is available from the detailed changelog which is available on the primap.org website and on zenodo.

Use of the dataset and full description

Before using the dataset, please read this document and the article describing the methodology, especially the section on uncertainties and the section on limitations of the method and use of the dataset.

Gütschow, J.; Jeffery, L.; Gieseke, R.; Gebel, R.; Stevens, D.; Krapp, M.; Rocha, M. (2016): The PRIMAP-hist national historical emissions time series, Earth Syst. Sci. Data, 8, 571-603, doi:10.5194/essd-8-571-2016

Please notify us (mail@johannes-guetschow.de) if you use the dataset so that we can keep track of how it is used and take that into consideration when updating and improving the dataset.

When using this dataset or one of its updates, please cite the DOI of the precise version of the dataset used and also the data description article which this dataset is supplement to (see above). Please consider also citing the relevant original sources when using the PRIMAP-hist dataset. See the full citations in the References section further below.

Since version 2.3 we use the data formats developed for the PRIMAP2 climate policy analysis suite: PRIMAP2 on GitHub. The data are published both in the interchange format which consists of a csv file with the data and a yaml file with additional metadata and the native NetCDF based format. For a detailed description of the data format we refer to the PRIMAP2 documentation.

We have also included files with more than three significant digits. These files are mainly aimed at people doing policy analysis using the country reported data scenario (HISTCR). Using the high precision data they can avoid questions on discrepancies with the reported data. The uncertainties of emissions data do not justify the additional significant digits and they might give a false sense of accuracy, so please use this version of the dataset with extra care.

Support

If you encounter possible errors or other things that should be noted, please check our issue tracker at github.com/JGuetschow/PRIMAP-hist and report your findings there. Please use the tag "v2.6" in any issue you create regarding this dataset.

If you need support in using the dataset or have any other questions regarding the dataset, please contact johannes.guetschow@climate-resource.com.

Climate Resource makes this data available CC BY 4.0 licence. Free support is limited to simple questions and non-commercial users. We also provide additional data, and data support services to clients wanting more frequent updates, additional metadata or to integrate these datasets into their workflows. Get in touch at contact@climate-resource.com if you are interested.

Sources

• Global CO2 emissions from cement production v240517 data, paper: Andrew
  (2024), Andrew (2019)
• EI Statistical Review of World Energy website: Energy Institute (2024)
• CDIAC data: Hefner and Marland (2023), data: Hefner (2024), paper: Gilfillan and Marland (2021)
• CEDS: data: Hoesly et al. (2020), paper: Hoesly et al. (2018)
• EDGAR version 8.0: data/website: European Commission, JRC (2023), report: European Commission. Joint Research Centre. (2023)
• EDGAR version 7.0: data, website, Reports: JRC (2022), reports: European
  Commission Joint Research Centre (2022), European Commission Joint Research Centre (2021),
• EDGAR-HYDE 1.4 data: Van Aardenne et al. (2001), Olivier and Berdowski (2001)
• FAOSTAT database data: Food and Agriculture Organization of the United Nations (2024)
• RCP historical data data, paper: Meinshausen et al. (2011)
• UNFCCC National Communications and National Inventory Reports for developing countries available from the UNFCCC DI portal website, data: UNFCCC (2024e), Pflüger and Gütschow (2024)
• UNFCCC Bnnial Update Reports, National Communications, and National Inventory
  Reports for developing countries website-BURs, <a

Seawater was collected via Niskin bottles mounted with a CTD from the San Pedro Ocean Time-series (SPOT) station off the coast of Southern California near the surface (5 m), 150 and 890 m, in late May 2015. Raw sequence data was generated as part of a metatranscriptome study targeting the protistan community. Raw sequences are available at the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) database (SRA Study ID: SRP110974, BioProject: PRJNA391503). Sequences for BioProject PRJNA608423 will be available at NCBI on Jan 1st, 2021.\r \r These data were published in Hu et al. (2018). access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv acquisition_description=Seawater was collected from the San Pedro Ocean Time-series (SPOT) station off the coast of Southern California near the surface (5 m), 150 and 890 m, in late May 2015. Briefly, seawater was pre-filtered (80 mm) into 20 L carboys to minimize the presence of multicellular eukaryotes. Replicate samples (ranging in volume from 1.5-3.5 L) from each depth were filtered onto sterile GF/F filters (nominal pore size 0.7 mm, Whatman, International Ltd. Florham Park, NJ). While we cannot avoid some impact that sample handling (i.e., bringing samples to the surface) may have had on our results, filters were immediately placed in 1.5 mL of lysis buffer and flash frozen in liquid nitrogen in < 40 min and away from light to minimize RNA degradation.

Total RNA was extracted from each filter using a DNA/RNA AllPrep kit (Qiagen, Valencia, CA, #80204) with an in-line genomic DNA removal step (RNase-free DNase reagents, Qiagen #79254) (dx.doi.org/10.17504/protocols.io.hk3b4yn). Extracted RNA was quality checked and low biomass samples were pooled. Six replicates were processed and sequenced from the surface, while pairs of filters were pooled for either 150 or 890 m, yielding 3 and 4 replicates respectively (Supporting Information Table S1). RNA concentrations were normalized before library preparation (Supporting Information). ERCC spike-in was added before sequence library preparation with Kapa\u2019s Stranded mRNA library preparation kit using poly-A tail selection beads to select for eukaryotic mRNA (Kapa Biosystems, Inc., Wilmington, MA, #KK8420).

Also see:

"%5C%22https://www.protocols.io/view/sample-collection-from-the-field-%0Afor-downstream-mo-hisb4eehttps://www.protocols.io/view/rna-and-optional-dna-%0Aextraction-from-environmental-hk3b4yn%5C%22">https://www.protocols.io/view/sample-collection-from-the-field-for- downs...

The associated assembly files can be found at Zenodo (see Hu, S. K. (2017), DOI:\u00a010.5281/zenodo.1202041).\u00a0 The assembly files were also published in the journal publication Hu, et al. (2018).

Related code can be found in the github repository https://github.com/shu251/SPOT_metatranscriptome.\u00a0 The version of the code used for these publications can be found in the Supplemental Files section of this page. awards_0_award_nid=743048 awards_0_award_number=OCE-1737409 awards_0_data_url=http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1737409 awards_0_funder_name=NSF Division of Ocean Sciences awards_0_funding_acronym=NSF OCE awards_0_funding_source_nid=355 awards_0_program_manager=David L. Garrison awards_0_program_manager_nid=50534 cdm_data_type=Other comment=Microbial eukaryotic focused metatranscriptome data PI: David Caron Data version 2: 2020-02-26 Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 dataset_current_state=Final and no updates defaultDataQuery=&time<now doi=10.26008/1912/bco-dmo.745518.2 infoUrl=https://www.bco-dmo.org/dataset/745518 institution=BCO-DMO instruments_0_acronym=Niskin bottle instruments_0_dataset_instrument_nid=773599 instruments_0_description=A Niskin bottle (a next generation water sampler based on the Nansen bottle) is a cylindrical, non-metallic water collection device with stoppers at both ends. The bottles can be attached individually on a hydrowire or deployed in 12, 24, or 36 bottle Rosette systems mounted on a frame and combined with a CTD. Niskin bottles are used to collect discrete water samples for a range of measurements including pigments, nutrients, plankton, etc. instruments_0_instrument_external_identifier=https://vocab.nerc.ac.uk/collection/L22/current/TOOL0412/ instruments_0_instrument_name=Niskin bottle instruments_0_instrument_nid=413 instruments_1_acronym=Automated Sequencer instruments_1_dataset_instrument_description=HiSeq High Output 125 bp PE sequencing was performed at UPC Genome Core at University of Southern California, Los Angeles, CA (BioProject: PRJNA391503). instruments_1_dataset_instrument_nid=745534 instruments_1_description=General term for a laboratory instrument used for deciphering the order of bases in a strand of DNA. Sanger sequencers detect fluorescence from different dyes that are used to identify the A, C, G, and T extension reactions. Contemporary or Pyrosequencer methods are based on detecting the activity of DNA polymerase (a DNA synthesizing enzyme) with another chemoluminescent enzyme. Essentially, the method allows sequencing of a single strand of DNA by synthesizing the complementary strand along it, one base pair at a time, and detecting which base was actually added at each step. instruments_1_instrument_name=Automated DNA Sequencer instruments_1_instrument_nid=649 instruments_1_supplied_name=HiSeq metadata_source=https://www.bco-dmo.org/api/dataset/745518 param_mapping={'745518': {}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/745518/parameters people_0_affiliation=University of Southern California people_0_affiliation_acronym=USC people_0_person_name=David Caron people_0_person_nid=50524 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=University of Southern California people_1_affiliation_acronym=USC people_1_person_name=Sarah K. Hu people_1_person_nid=745520 people_1_role=Co-Principal Investigator people_1_role_type=originator people_2_affiliation=University of Southern California people_2_affiliation_acronym=USC people_2_person_name=Sarah K. Hu people_2_person_nid=745520 people_2_role=Contact people_2_role_type=related people_3_affiliation=Woods Hole Oceanographic Institution people_3_affiliation_acronym=WHOI BCO-DMO people_3_person_name=Amber D. York people_3_person_nid=643627 people_3_role=BCO-DMO Data Manager people_3_role_type=related project=SPOT projects_0_acronym=SPOT projects_0_description=Planktonic marine microbial communities consist of a diverse collection of bacteria, archaea, viruses, protists (phytoplankton and protozoa) and small animals (metazoan). Collectively, these species are responsible for virtually all marine pelagic primary production where they form the basis of food webs and carry out a large fraction of respiratory processes. Microbial interactions include the traditional role of predation, but recent research recognizes the importance of parasitism, symbiosis and viral infection. Characterizing the response of pelagic microbial communities and processes to environmental influences is fundamental to understanding and modeling carbon flow and energy utilization in the ocean, but very few studies have attempted to study all of these assemblages in the same study. This project is comprised of long-term (monthly) and short-term (daily) sampling at the San Pedro Ocean Time-series (SPOT) site. Analysis of the resulting datasets investigates co-occurrence patterns of microbial taxa (e.g. protist-virus and protist-prokaryote interactions, both positive and negative) indicating which species consistently co-occur and potentially interact, followed by examination gene expression to help define the underlying mechanisms. This study augments 20 years of baseline studies of microbial abundance, diversity, rates at the site, and will enable detection of low-frequency changes in composition and potential ecological interactions among microbes, and their responses to changing environmental forcing factors. These responses have important consequences for higher trophic levels and ocean-atmosphere feedbacks. The broader impacts of this project include training graduate and undergraduate students, providing local high school student with summer lab experiences, and PI presentations at local K-12 schools, museums, aquaria and informal learning centers in the region. Additionally, the PIs advise at the local, county and state level regarding coastal marine water quality. This research project is unique in that it is a holistic study (including all microbes from viruses to small metazoa) of microbial species diversity and ecological activities, carried out at the SPOT site off the coast of southern California. In studying all microbes simultaneously, this work aims to identify important ecological interactions among microbial species, and identify the basis(es) for those interactions. This research involves (1) extensive analyses of prokaryote (archaean and bacterial) and eukaryote (protistan and micro-metazoan) diversity via the sequencing of marker genes, (2) studies of whole-community gene expression by eukaryotes and prokaryotes in order to identify key functional characteristics of microorganismal groups and the detection of active viral infections, and (3) metagenomic analysis of viruses and bacteria to aid interpretation of transcriptomic analyses using genome-encoded information. The project includes exploratory metatranscriptomic analysis of poorly-understood aphotic and hypoxic-zone protists, to examine their stratification, functions and hypothesized prokaryotic symbioses. projects_0_end_date=2021-07 projects_0_geolocation=San Pedro Channel off the coast of Los Angeles projects_0_name=Protistan, prokaryotic, and viral processes at the San Pedro Ocean

Raw DNA and RNA V4 tag sequences include spatially and temporally distinct samples from coastal California. Samples were collected in Niskin bottles with a CTD rosette at the San Pedro Ocean Time-series (SPOT) between April of 2013 and January of 2014. This dataset contains sequence data accession numbers and metadata for the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) database (SRA Study ID: SRP070577, BioProject: PRJNA311248). access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson acquisition_description=Samples were collected seasonally at the San Pedro Ocean Time-series (SPOT) station at four depths (surface, subsurface chlorophyll maximum, 150 and 890 m).\u00a0The SPOT station was sampled from 5 m, the subsurface chlorophyll maximum (SCM), 150 and 890 m using 10 L Niskin bottles mounted on a CTD rosette, during regularly scheduled cruises (https://dornsife.usc.edu/spot/).

Seawater from all samples was sequentially pre-filtered through 200 \u03bcm and 80 \u03bcm Nitex mesh to reduce abundances of multicellular eukaryotes (metazoa). Near-surface and SCM seawater (2 L) and 150 and 890 m seawater (4 L) was filtered onto GF/F filters (nominal pore size 0.7 \u03bcm; Whatman, International Ltd, Florham Park, NJ, USA) and immediately flash frozen in liquid nitrogen for later DNA and RNA extraction.

Total DNA and RNA were extracted simultaneously from each sample using the All Prep DNA/RNA Mini kit (Qiagen, Valencia, CA, USA, #80204). Genomic DNA was removed during the RNA extraction with RNase-Free DNase reagents (Qiagen,

79254). Total extracted RNA was checked for residual genomic DNA by

performing a polymerase chain reaction (PCR) using DNA specific primers to ensure that no amplified products appeared when run on an agarose gel. RNA was reverse transcribed into cDNA using iScript Reverse Transcription Supermix with random hexamers (Bio-Rad Laboratories, Hercules, CA, USA, #170-8840).

The resulting cDNA and DNA from each sample were PCR amplified using V4 forward (5\u2032\u00a0-CCAGCA[GC]C[CT]GCGGTA ATTCC-3\u2032\u00a0) and reverse (5\u2032\u00a0-ACTTTCGTTCTTGAT[CT][AG]A-3\u2032\u00a0) primers (Stoeck et al. 2010). Duplicate PCR reactions were performed in 50 \u03bcL volumes of: 1X Phusion High-Fidelity DNA buffer, 1 unit of Phusion DNA polymerase (New England Biolabs, Ipswich, MA, USA, #M0530S), 200 \u03bcM of dNTPs, 0.5 \u03bcM of each V4 forward and reverse primer, 3% DMSO, 50 mM of MgCl and 5 ng of either DNA or cDNA template per reaction. The PCR thermal cycler program consisted of a 98\u25e6C denaturation step for 30 s, followed by 10 cycles of 10 s at 98\u25e6C, 30 s at 53\u25e6C and 30 s at 72\u25e6C, and then 15 cycles of 10 s at 98\u25e6C, 30 s at 48\u25e6C and 30 s at 72\u25e6C, and a final elongation step at 72\u25e6C for 10 min, as described in Rodr\u0131 \u0301guez-Mart\u0131 \u0301nez et al. (2012). PCR products were purified (Qiagen, #28104) and duplicate samples were pooled. The \u223c400 bp cDNA and DNA PCR products were quality checked on an Agilent Bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA).

Sampling Locations:
SPOT (33\u25e6 33\u2032 N, 118\u25e6 24\u2032 W) - surface, DCM, 150 m, and 890 m
Port of LA (33\u25e6 42.75\u2032 N, 118\u25e6 15.55\u2032 W) - surface
Catalina (33\u25e6 27.17\u2032 N, 118\u25e6 28.51\u2032 W)-surface

For protocols see:
"%5C%22https://www.protocols.io/view/sample-collection-%0Afrom-the-field-for-downstream-mo-hisb4ee%5C%22">https://www.protocols.io/view/sample-collection-from-the-field-for- downstream-mo-hisb4ee
"%5C%22https://www.protocols.io/view/rna-and-optional-dna-%0Aextraction-from-environmental-hk3b4yn%5C%22">https://www.protocols.io/view/rna-and-optional-dna-extraction-from- environmental-hk3b4yn
"%5C%22https://www.protocols.io/view/18s-v4-tag-sequencing-pcr-%0Aamplification-and-librar-hdmb246%5C%22">https://www.protocols.io/view/18s-v4-tag-sequencing-pcr-amplification-and- librar-hdmb246 awards_0_award_nid=743048 awards_0_award_number=OCE-1737409 awards_0_data_url=http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1737409 awards_0_funder_name=NSF Division of Ocean Sciences awards_0_funding_acronym=NSF OCE awards_0_funding_source_nid=355 awards_0_program_manager=David L. Garrison awards_0_program_manager_nid=50534 cdm_data_type=Other comment=18S rRNA gene tag sequences PI: David Caron Data version 1: 2018-09-05 Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 defaultDataQuery=&time<now doi=10.1575/1912/bco-dmo.745527.1 Easternmost_Easting=-118.26 geospatial_lat_max=33.72 geospatial_lat_min=33.45 geospatial_lat_units=degrees_north geospatial_lon_max=-118.26 geospatial_lon_min=-118.48 geospatial_lon_units=degrees_east infoUrl=https://www.bco-dmo.org/dataset/745527 institution=BCO-DMO instruments_0_acronym=Niskin bottle instruments_0_dataset_instrument_nid=745591 instruments_0_description=A Niskin bottle (a next generation water sampler based on the Nansen bottle) is a cylindrical, non-metallic water collection device with stoppers at both ends. The bottles can be attached individually on a hydrowire or deployed in 12, 24 or 36 bottle Rosette systems mounted on a frame and combined with a CTD. Niskin bottles are used to collect discrete water samples for a range of measurements including pigments, nutrients, plankton, etc. instruments_0_instrument_external_identifier=https://vocab.nerc.ac.uk/collection/L22/current/TOOL0412/ instruments_0_instrument_name=Niskin bottle instruments_0_instrument_nid=413 instruments_1_acronym=CTD SBE 911plus instruments_1_dataset_instrument_nid=745592 instruments_1_description=The Sea-Bird SBE 911plus is a type of CTD instrument package for continuous measurement of conductivity, temperature and pressure. The SBE 911plus includes the SBE 9plus Underwater Unit and the SBE 11plus Deck Unit (for real-time readout using conductive wire) for deployment from a vessel. The combination of the SBE 9plus and SBE 11plus is called a SBE 911plus. The SBE 9plus uses Sea-Bird's standard modular temperature and conductivity sensors (SBE 3plus and SBE 4). The SBE 9plus CTD can be configured with up to eight auxiliary sensors to measure other parameters including dissolved oxygen, pH, turbidity, fluorescence, light (PAR), light transmission, etc.). more information from Sea-Bird Electronics instruments_1_instrument_external_identifier=https://vocab.nerc.ac.uk/collection/L22/current/TOOL0058/ instruments_1_instrument_name=CTD Sea-Bird SBE 911plus instruments_1_instrument_nid=591 instruments_2_acronym=Automated Sequencer instruments_2_dataset_instrument_nid=745568 instruments_2_description=General term for a laboratory instrument used for deciphering the order of bases in a strand of DNA. Sanger sequencers detect fluorescence from different dyes that are used to identify the A, C, G, and T extension reactions. Contemporary or Pyrosequencer methods are based on detecting the activity of DNA polymerase (a DNA synthesizing enzyme) with another chemoluminescent enzyme. Essentially, the method allows sequencing of a single strand of DNA by synthesizing the complementary strand along it, one base pair at a time, and detecting which base was actually added at each step. instruments_2_instrument_name=Automated DNA Sequencer instruments_2_instrument_nid=649 instruments_2_supplied_name=Illumina MiSeq metadata_source=https://www.bco-dmo.org/api/dataset/745527 Northernmost_Northing=33.72 param_mapping={'745527': {'lat': 'master - latitude', 'lon': 'master - longitude'}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/745527/parameters people_0_affiliation=University of Southern California people_0_affiliation_acronym=USC people_0_person_name=David Caron people_0_person_nid=50524 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=University of Southern California people_1_affiliation_acronym=USC people_1_person_name=Sarah K Hu people_1_person_nid=745520 people_1_role=Co-Principal Investigator people_1_role_type=originator people_2_affiliation=University of Southern California people_2_affiliation_acronym=USC people_2_person_name=Sarah K Hu people_2_person_nid=745520 people_2_role=Contact people_2_role_type=related people_3_affiliation=Woods Hole Oceanographic Institution people_3_affiliation_acronym=WHOI BCO-DMO people_3_person_name=Amber York people_3_person_nid=643627 people_3_role=BCO-DMO Data Manager people_3_role_type=related project=SPOT projects_0_acronym=SPOT projects_0_description=Planktonic marine microbial communities consist of a diverse collection of bacteria, archaea, viruses, protists (phytoplankton and protozoa) and small animals (metazoan). Collectively, these species are responsible for virtually all marine pelagic primary production where they form the basis of food webs and carry out a large fraction of respiratory processes. Microbial interactions include the traditional role of predation, but recent research recognizes the importance of parasitism, symbiosis and viral infection. Characterizing the response of pelagic microbial communities and processes to environmental influences is fundamental to understanding and modeling carbon flow and energy utilization in the ocean, but very few studies have attempted to study all of these assemblages in the same study. This project is comprised of long-term (monthly) and short-term (daily) sampling at the San Pedro Ocean Time-series (SPOT) site. Analysis of the resulting datasets investigates co-occurrence patterns of microbial taxa (e.g. protist-virus and protist-prokaryote interactions, both positive and negative) indicating which species consistently co-occur and potentially interact, followed by examination gene expression to help define the underlying mechanisms. This study augments 20 years of baseline studies of microbial abundance, diversity, rates at the site, and will enable

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