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1,118 results for “Time series”
Subsystem Discovery in High-Dimensional Time-Series Using Masked Autoencoders
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"Wing Inertia Influences the Phase and Amplitude Relationships Between Thorax Deformation and Flapping Angle in Bumblebees"-Time Series Data
<p>This file contains all supporting data for the study titled "Wing Inertia Influences the Phase and Amplitude Relationships<br>Between Thorax Deformation and Flapping Angle in Bumblebees" By Braden Cote, Cailin Casey, and Mark Jankauski</p>
Total Solar Irradiance (TSI) in the satellite era: Multiple new or updated satellite TSI composites and related time series
<h2>Supporting dataset to accompany R. Connolly et al. (2024)</h2> <h3><strong>Associated journal article:</strong></h3> <div>R. Connolly, W. Soon, M. Connolly, R.G. Cionco, A.G. Elias, G.W. Henry, N. Scafetta, and V.M. Velasco Herrera (2024). Multiple new or updated satellite Total Solar Irradiance (TSI) composites (1978-2023). The Astrophysical Journal. <strong>975 </strong>(1), 102. <a href="https://doi.org/10.3847/1538-4357/ad7794">https://doi.org/10.3847/1538-4357/ad7794</a></div> <div> </div> <h3><strong>Description of contents</strong>:</h3> <div>An Excel spreadsheet with 25 tabs. All of the TSI composite time series, the proxy-based models used for comparison, and the data used to generate the figures and tables are provided in this sheet. The first tab describes the contents of the remaining tabs.</div> <div> </div> <h3><strong>System requirements</strong>:</h3> <div>MS Excel or another program that can read .xlsx files.</div> <div> </div> <h3><strong>Additional comments</strong>:</h3> <div>Version 1.0 is a mirror archive of the original Supporting Data for the journal article described above. Updates to this dataset will be provided here at <a href="https://doi.org/10.5281/zenodo.13619470">https://doi.org/10.5281/zenodo.13619470</a> or on the CERES-Science website at <a href="https://www.ceres-science.com/portfolio-collections/solar-activity/total-solar-irradiance-in-the-satellite-era">https://www.ceres-science.com/portfolio-collections/solar-activity/total-solar-irradiance-in-the-satellite-era</a>. </div> <div> </div> <h3><strong>Citation request</strong>:</h3> <div>We ask that users of this dataset include an appropriate citation to the R. Connolly et al. (2024, The Astrophysical Journal) article in any publications that make use of the dataset. If possible, we encourage including a direct citation to this dataset as well as to the article. We recommend including the version number and/or download date if you are using an update from the original dataset.</div> <div> </div> <div>=====</div>
Datasets of Aerosol properties retrieved from AOD time series at selected GAWPFR stations
<p>The datasets of aerosol properties retrieved from Aerosol Optical Depth (AOD) measurements from Precision Filter Radiometers (PFR) using the model Generalized Retrieval of Atmosphere and Surface Properties-Aerosol Optical Depth (GRASP-AOD). They correspond to 3 stations of the GAW-PFR network (Davos, Izana and Hohenpeissenberg) and one associated (Lindenberg).</p> <p>Each file corresponds to a station (identifier DAV for Davos, HPB for Hohenpeissenberg and IZO for Izaña). The file names include the identifier for each station, the starting and ending year of the measurements. The first 4 lines of the file include information about the station and the 5th about the columns of the file. The following columns are included in every text file: date(1-3), time UTC (4-6), air mass (7), PFR AOD at 368, 412, 500 and 862 nm (8-11), retrieved PFR AOD from GRASP at 368, 412, 500 and 862 nm (12-15), Angström Exponent (16), Fine mode aerosol optical depth (17), Coarse mode aerosol optical depth (18), Total volume concentration (19), Fine mode volume concentration (20), Coarse mode volume concentration (21), Effective radius (22), Fine mode volume median radius (23), Coarse mode volume median radius (24), Fine mode geometric standard deviation (25), Coarse mode geometric standard deviation (26), Inversion absolute error (27), Inversion relative error (28).</p> <p>The dataset includes the full time series days per station. At stations there was a CIMEL sun photometer from AERONET measuring in parallel, which we used for the validation of the aerosol properties.</p> <p>We also include a report describing the work in more detail, including the validation process and results.</p>
FIGURE 3. Hypericum bilgehan-bilgilii. A. flowering time B–C. flower D. leaves E. habitat F in A new species from southern Anatolia (Dedegöl Mountain Series-Çürük Mountain) in Turkey: Hypericum bilgehan-bilgilii (Hypericaceae)
FIGURE 3. Hypericum bilgehan-bilgilii. A. flowering time B–C. flower D. leaves E. habitat F. fruit (capsule) G. seed (photos by A. Savran)
Total Electron Content and Magnetic Field TIme Series Survey Plots during THEMIS/CMO/FAIR Magnetic Conjunctions
<p>These survey plots are for time intervals corresponding to magnetic conjunctions between (1) NASA's Time History of Events and Macroscale Interactions during Substorms (THEMIS) satellites, (2) USGS College Alaska (CMO) magnetic observatory, and (3) the FAIR GNSS receiver near Fairbanks, Alaska. Magnetic field measurements are used from THEMIS (~3s sampling interval) and CMO (1s sampling interval), while 1s Total Electron Content (TEC) measurements are used from high-rate RINEX data for FAIR obtained from the NASA Crustal Dynamics Data Information System (CDDIS) archive of space geodesy data. The plots also contain solar wind measurements and geomagnetic activity indices from NASA's OMNIWeb (https://omniweb.gsfc.nasa.gov/) database for the same magnetic conjunction intervals. </p> <p>The time series stackplots during each magnetic conjunction show the three components of Interplanetary Magnetic Field, Sym-H index, THEMIS satellite magnetic field perturbation, CMO ground-based magnetometer magnetic field perturbation, TEC perturbation from different GPS satellites-FAIR receiver pairs (specific GPS satellite varies from event to event), and elevation angle for the same GPS satellites.</p>
Time series transcriptomes resolve metabolic pathways underlying crocin's anti-cancer activity: Control FASTQ sequencing reads
<p><span>Natural products like saffron show promise in treating hepatocellular carcinoma (HCC), but their mechanisms remain unclear. Here, we used time-series transcriptomics to elucidate crocin's anti-cancer mechanisms in HCC cells. We treated HepG2 cells with 1 and 2 mM crocin for 2, 6, 12, and 24 hours and analyzed transcriptomic profiles at each timepoint. The strongest transcriptional response occurred at 2 hours with 1 mM crocin, with diminishing effects at later timepoints. We observed upregulation of metabolic-, adhesion-, and endocytosis-related genes across all timepoints. Pathway analysis revealed activation of DNA damage checkpoints and senescence while proliferation pathways were suppressed. Notably, 52 genes involved in non-alcoholic fatty liver disease were downregulated at 24 hours (FDR p = 8 × 10⁻⁸), suggesting reversal of carcinogenic pathways. Strikingly, crocin consistently downregulated spliceosomal machinery genes across all timepoints while upregulating senescence and autophagy pathways. This spliceosome targeting represents a clinically relevant mechanism, as aberrant splicing drives oncogenesis in more than 90% of cancers. The transcription factor PAX5 was significantly upregulated while oncogenic ELK1 targets were downregulated. Our findings show that crocin treatment is accompanied by HCC cell senescence induction through coordinated spliceosome disruption and metabolic reprogramming, providing novel therapeutic targets for hepatocellular carcinoma.</span></p> <p><strong><span>Keywords: </span></strong><span>Hepatocellular carcinoma (HCC), crocin, transcriptomics, spliceosome, senescence, natural anti-cancer compounds</span></p>
Weather- and climate-driven power supply and demand time series for European countries
<p>This repository contains times series of wind power generation, solar power generation, hydropower inflow, heating demand, and cooling demand that use both historical and projected climate variables. Historical climate data are from the ERA5 reanalysis (1940-2023), while projected climate variables are from three climate models of the CMIP5 EURO-CORDEX (2006-2100) and three representative concentration pathways (RCP 2.6, RCP 4.5 and RCP 8.5). The time series are at country level for the EU27 countries, Balkan countries, the United Kingdom, Norway, and Switzerland.</p> <p>Each country has the following files for the historical period, where ** is the country’s ISO Alpha-2 code:</p> <p>1) **__ERA5__wind__capacity_factor_time_series__onshore.nc<br>2) **__ERA5__wind__capacity_factor_time_series__offshore.nc<br>3) **__ERA5__solar__capacity_factor_time_series.nc<br>4) **__ERA5__hydropower__inflow_time_series__conventional_and_pumped_storage.nc<br>5) **__ERA5__hydropower__inflow_time_series__run_of_river.nc<br>6) **__ERA5__heating__demand_time_series__residential_space.nc<br>7) **__ERA5__heating__demand_time_series__services_space.nc<br>8) **__ERA5__cooling__demand_time_series.nc</p> <p>Each country has also the following files for the climate projections, where ** is the country’s ISO Alpha-2 code, #_# is the value of the RCP scenario (e.g., 2_6 is RCP 2.6), ££££__&&&& is the name of the combination of global-regional climate models (e.g., CNRM_CERFACS_CM5__CNRM_ALADIN63):</p> <p>9) **__CORDEX__RCP_#_#__££££__&&&&__wind__capacity_factor_time_series__onshore.nc<br>10) **__CORDEX__RCP_#_#__££££__&&&&__wind__capacity_factor_time_series__offshore.nc<br>11) **__CORDEX__RCP_#_#__££££__&&&&__solar__capacity_factor_time_series.nc<br>12) **__CORDEX__RCP_#_#__££££__&&&&__hydropower__inflow_time_series__conventional_and_pumped_storage.nc<br>13) **__CORDEX__RCP_#_#__££££__&&&&__hydropower__inflow_time_series__run_of_river.nc<br>14) **__CORDEX__RCP_#_#__££££__&&&&__heating__demand_time_series__residential_space.nc<br>15) **__CORDEX__RCP_#_#__££££__&&&&__heating__demand_time_series__services_space.nc<br>16) **__CORDEX__RCP_#_#__££££__&&&&__cooling__demand_time_series.nc</p> <p>The repository also contains the caliration coefficients that resulted from the calibration procedure for the countries in which data were available.</p> <p> </p>
Downscaled climate time series
<p>Downscaled time series developed for the following paper: Fernández, Manquehual-Cheuque & Somos_Valenzuela (2024): Impact of Solar Radiation Management on Andean glacier-wide surface mass balance, npj Climate and Atmospheric Science, doi:<strong> </strong>10.1038/s41612-024-00807-x</p>
Generation-based and consumption-based grid emission intensity time series for German federal states 01/2020 - 07/2024
<p>The dataset contains time series estimating the consumption mix, generation-based and consumption-based grid emission intensity of German federal states for the period January 2020 to end of June 2024 as shown on the <a href="https://co2map.de">CO2Map</a> website. For an explanation of the underlying methodology see the <a href="https://co2map.de/methodology.html">CO2Map Methology</a> section, a detailed scientific article currently is in preparation.</p> <p>All time series have an hourly resolution (time stamps are given in UTC) and contain values for the 13 federal territorial states in Germany, with the city states merged into them:<br><br>BW: Baden-Wuerttemberg<br>BY: Bavaria<br>BB: Brandenburg and Berlin<br>HE: Hesse<br>MV: Mecklenburg-Western Pomerania<br>NI: Lower Saxony and Bremen<br>NW: North Rhine-Westphalia<br>RP: Rhineland-Palatinate<br>SL: Saarland<br>SN: Saxony<br>ST: Saxony-Anhalt<br>SH: Schleswig-Holstein and Hamburg<br>TH: Thuringia</p> <p><strong>"Emission intensity"</strong><br>This folder contains the following files for 2020 to 2023:<br>"region_consumption_based_emission_intensity.csv": Consumption-based emission intensity in kgCO2/kWh<br>"region_generation_based_emission_intensity.csv": Generation-based emission intensity in kgCO2/kWh</p> <p><strong>"Import tables"</strong><br>The folder contains the raw data representing the hourly consumption and storage mix including imports for federal states in MWh. This data allows to calculate generation-based and consumption-based emission intensity time series using arbitrary technology-specific emission factors for the technology types Biomass, Gas, Hard coal, Hydro, Lignite, Nuclear, Other fossil, Other renewable, Solar, Storage, Wind Offshore, Wind Onshore. Each file contains time series for one region as given above and for one year. The columns represent the hourly amount of load/storage/load+storage originating from a certain region and technology type, with the regions including local generation (i.e. the same federal state), imports from other federal states in Germany, and imports from other European countries (the origin of the imports is determined using a flow-tracing algorithm, see the <a href="https://co2map.de/methodology.html">CO2Map Methology</a> section).</p> <p>For each year and each federal state there is one file for load (representing electricity consumption), one file for storage (representing storage charging) and one file for total (sum of load and storage). The file "2023_import_table_load_DE_BW.csv", for instance, contains the mix of hourly load in Baden-Wuerttemberg. The first entry 01.01.2023 00:00 (UTC) then contains the mix for the electricity consumption in the first hour (UTC) of the year 2023 in BW. The sum over all columns for this row yields 4667.80 MWh, which is the approximated total load in this hour in BW (based on a regionalization method). The largest entry in the row is from "DE_NI,HB / Wind Onshore" (614.57 MWh), which is the amount of onshore wind power generated in Lower Saxony and Bremen and consumed in Baden-Wuerttemberg (based on the flow-tracing method). The second largest entry is from "DE_BW / Wind Onshore", which is the amount of onshore wind power generated and consumed locally in Baden-Wuerttemberg. The largest import to BW from other countries in this hour has been 115.18 MWh of nuclear power from Belgium. The file "2023_import_table_load_DE_BW.csv" contains an aggregated version of the same file, with the originating regions corresponding to the same federal state, imports from other federal states in Germany, and imports from other countries (each separated per technology).</p> <p><strong>"Export tables"<br></strong>The folder "Export tables" is similar to the one with "Import tables", but gives data about the location where generation from German regions is used. It contains the raw data representing the hourly generation mix associated with the location where it is used (locally and in other regions). The columns represent the hourly amount of load/storage/load+storage originating from the given region. </p> <p>For each year and each federal state there is one file for load (representing electricity consumption), one file for storage (representing storage charging) and one file for total (sum of load and storage). The file "2023_export_table_total_DE_SH,HH.csv", for instance, contains the mix of hourly generation in Schleswig-Holstein and Hamburg which is associated with electricity consumption and storage charging both locally and abroad. The first entry 01.01.2023 00:00 (UTC) then contains the amount of generation from SH,HH used in each region in the first hour (UTC) of the year 2023. The sum over all columns for this row yields 5730.40 MWh, which is the approximated total generation in this hour in SH,HH (based on a regionalization method). Since SH,HH is a net exporter in this hour, the sum over all columns associated with SH,HH represents the local load+storage charging (1842.40 MWh). The sum over all columns with "Wind Onshore" yields the generation from onshore wind in SH,HH in this hour (4318.05 MWh) (again based on a regionalization method). The different entries for "Wind Onshore" then tell where this onshore wind is used (load and storage charging), with the main contributions locally in SH,HH (1388.33 MWh), and exports to Norway (1582.03 MWh) and UK (682.67 MWh). The destination of these exports is estimated based on the flow tracing method in the same way as the origin of imports are determined.</p> <p>For the data sources and methods used to calculate the time series contained in this dataset see the CO2Map <a href="https://co2map.de/methodology.html">Methodology </a>and <a href="https://co2map.de/about.html">About </a>sections. </p> <p>Please communicate any questions, corrections or comments to mirko.schaefer [at] inatech.uni-freiburg.de</p> <p>All authors acknowledge funding from Elektrizitätswerke Schönau (EWS) through the Sonnencent program, ID 00009280. Tim Fürmann acknowledges funding from DFG (SPP 1984), project ID 450860949. Ramiz Qussous and Robin L. Grether would like to thank the German Federal Government, the German State Governments, and the Joint Science Conference (GWK) for their funding and support as part of the NFDI4Energy consortium, funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – 501865131.</p>
Building thermal and electrical energy hourly time series
<p>The results reported are the <strong>hourly thermal and electrical energy use</strong> for one year of a renovated five-floor small Multi-Family house with ten dwellings in different EU climates.</p> <p>A simulation-based database organized in Excel files (one per each climate) collects the hourly thermal and electrical energy use in the considered reference building (a small Multi-Family house) in three EU climates: Nordic, EU Continental, and Mediterranean considering the HVAC system solution with room booster heat pumps (HPs) developed within Horizon 2020 HAPPENING project and two reference systems. </p> <p>The building, HVAC and on-site renewable energy production systems are modeled and simulated using TRNSYS as dynamic simulation software. </p> <p>The fact that this simulation work is performed in different EU climates allows to extrapolate indications about how the HVAC system solutions work in different EU climatic contexts.</p> <p>Regarding the <strong>building</strong>, a small Multi-Family house is considered as it represents the target building for the HP-base solutions developed within the HAPPENING project. The building has five floors with two dwellings per floor with a floor area of 50 m<sup>2</sup> each, for a total floor area of 500 m<sup>2</sup>. In terms of thermal envelope performance, a renovated building has been considered, with a yearly space heating thermal demand in the range of 70 kWh/m<sup>2</sup>/y in the Nordic and EU Continental climates.</p> <p>Regarding the <strong>HVAC system</strong> solutions, the dataset reports the results obtained considering the cascade HP-based solution with room booster HPs developed in the project HAPPENING. In addition to that, a typical (reference) air-water HP system, and a gas boiler system are used as reference systems to assess and compare the HAPPENING system solution performance.</p> <p>Regarding the <strong>on-site renewable energy production system</strong>, a PV plant has been considered. The presence of an electric battery is also accounted for to allow a more complete assessment of the interactions between on-site renewable production and HVAC system consumption, highlighting the effects, advantages, and disadvantages of having also an electric storage. The PV and battery system is considered in all HVAC system solutions (HAPPENING solution, reference air-water HP system, gas boiler system) to highlight the differences in the interactions between different HVAC system solutions with PV and battery.</p> <p>The project deliverable D2.7, available in the material, reports more details about all the quantities included in the dataset, as well as a series of system performance analyses, both over representative weeks and on a yearly basis.</p> <p> </p> <p>The research leading to these results has received funding from the European Community's Horizon 2020 Programme (H2020) under grant agreement n° 957007.</p>
Data from: Which specimens from a museum collection will yield DNA barcodes? A time series study of spiders in alcohol
We report initial results from an ongoing effort to build a library of DNA barcode sequences for Dutch spiders and investigate the utility of museum collections as a source of specimens for barcoding spiders. Source material for the library comes from a combination of specimens freshly collected in the field specifically for this project and museum specimens collected in the past. For the museum specimens, we focus on 31 species that have been frequently collected over the past several decades. A series of progressively older specimens representing these 31 species were selected for DNA barcoding. Based on the pattern of sequencing successes and failures, we find that smaller-bodied species expire before larger-bodied species as tissue sources for single-PCR standard DNA barcoding. Body size and age of oldest successful DNA barcode are significantly correlated after factoring out phylogenetic effects using independent contrasts analysis. We found some evidence that extracted DNA concentration is correlated with body size and inversely correlated with time since collection, but these relationships are neither strong nor consistent. DNA was extracted from all specimens using standard destructive techniques involving the removal and grinding of tissue. A subset of specimens was selected to evaluate nondestructive extraction. Nondestructive extractions significantly extended the DNA barcoding shelf life of museum specimens, especially small-bodied species, and yielded higher DNA concentrations compared to destructive extractions. All primary data are publically available through a Dryad archive and the Barcode of Life database.
Genomic time-series data show that gene flow maintains high genetic diversity despite substantial genetic drift in a butterfly species
<p>Effective population size affects the efficacy of selection, rate of evolution by drift, and neutral diversity levels. When species are subdivided into multiple populations connected by gene flow, evolutionary processes can depend on global or local effective population sizes. Theory predicts that high levels of diversity might be maintained by gene flow, even very low levels of gene flow, consistent with species long-term effective population size, but tests of this idea are mostly lacking. Here, we show that Lycaeides butterfly populations maintain low contemporary (variance) effective population sizes (e.g., ~200 individuals) and thus evolve rapidly by genetic drift. In contrast, populations harbored high levels of genetic diversity consistent with an effective population size several orders of magnitude larger. We hypothesized that the differences in the magnitude and variability of contemporary versus long-term effective population sizes were caused by gene flow of sufficient magnitude to maintain diversity but only subtly affect evolution on generational time scales. Consistent with this hypothesis, we detected low but non-trivial gene flow among populations. Furthermore, using short-term population-genomic time-series data, we documented patterns consistent with predictions from this hypothesis, including a weak but detectable excess of evolutionary change in the direction of the mean (migrant gene pool) allele frequencies across populations, and consistency in the direction of allele frequency change over time. The documented decoupling of diversity levels and short-term change by drift in Lycaeides has implications for our understanding of contemporary evolution and the maintenance of genetic variation in the wild.</p>
GISD30: global 30-m impervious surface dynamic dataset from 1985 to 2020 using time-series Landsat imagery on the Google Earth Engine platform
<p>A novel and accurate global 30 m impervious surface dynamic dataset (GISD30) for 1985 to 2020 was produced using the spectral generalization method and time-series Landsat imagery, on the Google Earth Engine cloud-computing platform.</p>
Long Time-Series Glacier Outlines in the Three-Rivers Headwater Region from 1986 to 2021 Based on Deep Learning
<p>The glacier outlines in the Three-Rivers Headwater Region from 1986–2021 in a total of 12 periods were obtained based on Landsat-5 and 8 images using the M-LandsNet and through manual adjustments. The comparison with previous research results indicated that this dataset has high accuracy. This dataset can provide support for the study of the glaciers mass balance in the Three-Rivers Headwater Region.</p>
Time-series drinking water metagenomes: Assemblies & MAGs
<p><span><span><span><span><span><span><span><span><span><span><span>Reconstructing microbial genomes from metagenomic short-read data can be challenging due to the unknown and uneven complexity of microbial communities. This complexity encompasses highly diverse populations which often includes strain variants. Reconstructing high-quality genomes is a crucial part of the metagenomic workflow as subsequent ecological and metabolic inferences depend on their accuracy, quality and completeness. In contrast to microbial communities in other ecosystems, there has been no systematic assessment of genome-centric metagenomic workflows for drinking water microbiomes. In this study, we assessed the performance of a combination of assembly and binning strategies for time-series drinking water metagenomes that were collected over 6 months. The goal of this study was to identify the combination of assembly and binning approaches that results in high quality and quantity metagenome-assembled genomes (MAGs), representing most of the sequenced metagenome. Our findings suggest that the metaSPAdes co-assembly strategies had the best performance as they resulted in larger and less fragmented assemblies with at least 85% of the sequence data mapping to contigs greater than 1kbp. Furthermore, a combination of metaSPAdes co-assembly strategies and MetaBAT2 produced the highest number of medium-quality MAGs while capturing at least 70% of the metagenomes based on read recruitment. Utilizing different assembly/binning approaches also assist in the reconstruction of unique MAGs from closely related species that would have otherwise collapsed into a single MAG using a single workflow. Overall, our study suggests that leveraging multiple binning approaches with different metaSPAdes co-assembly strategies may be required to maximize the recovery of good-quality MAGs.</span></span></span></span></span></span></span></span></span></span></span></p>
The PRIMAP-hist national historical emissions time series (1750-2024) v2.7
<p><strong>Recommended citation</strong></p> <p>Gütschow, J.; Busch, D.; Pflüger, M. (2025): The PRIMAP-hist national historical emissions time series v2.7 (1750-2024). zenodo. doi:10.5281/zenodo.<em>17090760</em>.</p> <p>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</p> <p><strong>Content</strong></p> <ul> <li><a href="#use-of-the-dataset-and-full-description">Use of the dataset and full description</a></li> <li><a href="#abstract">Abstract</a></li> <li><a href="#support">Support</a></li> <li><a href="#sources">Sources</a></li> <li><a href="#files-included-in-the-dataset">Files included in the dataset</a></li> <li><a href="#notes">Notes</a></li> <li><a href="#data-format-description-columns">Data format description (columns)</a></li> <li><a href="#references">References</a></li> <li><a href="#changelog">Changelog</a></li> </ul> <p><strong>Abstract</strong></p> <p>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 2024, 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.</p> <p>For developed countries, AnnexI in terms of the UNFCCC, this is the data submitted anually in the "National Inventory Submissions". Until 2023 data was submitted in the "Common Reporting Format" (CRF). Since 2024 the new "Common Reporting Tables" (CRT) are used.For developing countries, non-AnnexI in terms of the UNFCCC our preferred data source are the Common Reporting Tables (CRT) submitted with the Biannial Transparency Reports (BTR). When countries do not provide the tables we read available data from the pdf reports and use additional submissions (Biannial Update Reports (BUR), National Communications (NC), and National Inventory Reports (NIR)) read from pdf and xlsx/csv files and for older submissions obtained from the UNFCCC DI portal (di.unfccc.int). For a list of these submissions please see below. For South Korea the 2024 official GHG inventory has not yet been submitted to the UN but is included in PRIMAP-hist. PRIMAP-hist also includes official data for Taiwan which is not recognized as a party to the UNFCCC. As the USA have not submitted any data to the UNFCCC this year we use the draft inventory report which the Environmental Defense Fund (EDF) obtained from the US Environmental Protection Agency (EPA) through the Freedom of Information Act.</p> <p>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 2024 (all sectors for CO2, CH4, N2O, HFCs, PFCs, SF6, NF3, except energy CO2). Lower priority data are harmonized to higher priority data in the gap-filling process.</p> <p>For the third party priority time series gaps in the third party data are filled from country reported data sources.</p> <p>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.</p> <p>The v2.4 release of PRIMAP-hist reduced the time-lag from 2 to 1 years for the October release. Thus the present version 2.7 includes data for 2024. For energy CO2 growth rates from the Energy Institute's *Statistical Review of World Energy* are used to extend the country reported data to 2024. For CO2 from cement production Andrew cement data are used for a few countries. For all other sectors and gases no emission estimates exist. Thus PRIMAP-hist relies on numerical methods and uses a linear extrapolation based on the last 5 years. COVID-19 has primarily impacted energy related emissions and in tests with CRF data no impact of COVID in the performance of linear extrapolation of emissions data in the other sectors has been detected. For the few cases where extrapolation is needed for energy CO2 we use a 15 year trend for the extrapolation.</p> <p>Version 2.7 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.</p> <p>The PRIMAP-hist v2.7 dataset is an updated version of</p> <p>Gütschow, J.; Pflüger, M.; Busch, D. (2025): The PRIMAP-hist national historical emissions time series v2.6.1 (1750-2023). zenodo. doi:10.5281/zenodo.15016289.</p> <p>The <a href="#changelog">Changelog</a> indicates the most important changes. You can also check the issue tracker on <a href="https://github.com/JGuetschow/PRIMAP-hist">github.com/JGuetschow/PRIMAP-hist</a> 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 <a href="https://primap.org/primap-hist">primap.org</a> website and on zenodo.</p> <p><strong>Use of the dataset and full description</strong></p> <p>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.</p> <p>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</p> <p>Please notify us (support@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.</p> <p>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.</p> <p>Since version 2.3 we use the data formats developed for the PRIMAP2 climate policy analysis suite: <a href="https://github.com/pik-primap/primap2">PRIMAP2 on GitHub</a>. 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 <a href="https://primap2.readthedocs.io/en/stable/">PRIMAP2 documentation</a>.</p> <p>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.</p> <p><strong>Support</strong></p> <p>If you encounter possible errors or other things that should be noted, please check our issue tracker at <a href="https://github.com/JGuetschow/PRIMAP-hist">github.com/JGuetschow/PRIMAP-hist</a> and report your findings there. Please use the tag "v2.7" in any issue you create regarding this dataset.</p> <p>If you need support in using the dataset or have any other questions regarding the dataset, please contact nc-support@johannes-guetschow.de.</p> <p>Basic support is free for non-commercial users and most questions can be answered with a short e-mail. However, we do not have the resources to provide extensive support free of charge. For commercial users support will be included with the commercial license (see below).</p> <p><strong>License</strong></p> <p>Since v2.7 PRIMAP-hist is published under a non-commercial license (CC BY-NC-SA). This means that commercial users can not use it freely and have to obtain a commercial license. The commercial license is only available for the country reported priority (CR) time-series as the third party priority (TP) time-series builds heavily on EDGAR and FAOSTAT data. For commercial customers wanting to use the TP time-series we offer to develop custom code to generate the data locally. Please contact commercial-support@johannes-guetschow.de for more information.</p> <p><strong>Sources</strong></p> <ul> <li><strong>Global CO2 emissions from cement production v250226</strong> <a href="https://doi.org/10.5281/zenodo.14931651">data</a>, <a href="https://doi.org/10.5194/essd-11-1675-2019">paper</a>: Andrew<br>(2025), Andrew (2019)</li> <li><strong>EI Statistical Review of World Energy 2025</strong> <a href="https://www.energyinst.org/statistical-review">website</a>: Energy Institute (2025)</li> <li><strong>CDIAC</strong> <a href="https://energy.appstate.edu/cdiac-appstate/data-products">data</a>: Hefner and Marland (2023), <a href="https://doi.org/10.5281/zenodo.10607882">data</a>: Hefner (2024), <a href="https://essd.copernicus.org/articles/13/1667/2021/">paper</a>: Gilfillan and Marland (2021)</li> <li><strong>CEDS</strong>: <a href="http://doi.org/10.5281/zenodo.3606753">data:</a> Hoesly et al. (2020), <a href="https://gmd.copernicus.org/articles/11/369/2018/">paper</a>: Hoesly et al. (2018)</li> <li><strong>EDGAR 2024</strong>: <a href="https://edgar.jrc.ec.europa.eu/report_2024">data/website</a>: European Commission, European Commision, JRC (2024), report: European Commission. Joint Research Centre & IEA. (2024)</li> <li><strong>EDGAR-HYDE 1.4</strong> <a href="http://themasites.pbl.nl/tridion/en/themasites/edgar/emission_data/edgar-hyde-100yr/edgar-hyde-1-4.html">data</a>: Van Aardenne et al. (2001), Olivier and Berdowski (2001)</li> <li><strong>FAOSTAT database</strong> <a href="http://www.fao.org/faostat/en/#data">data</a>: Food and Agriculture Organization of the United Nations (2024)</li> <li><strong>RCP historical data</strong> <a href="http://www.pik-potsdam.de/~mmalte/rcps/">data</a>, <a href="https://doi.org/10.1007/s10584-011-0156-z">paper</a>: Meinshausen et al. (2011)</li> <li><strong>UNFCCC National Communications and National Inventory Reports for developing countries available from the UNFCCC DI portal</strong> <a href="http://di.unfccc.int/detailed_data_by_party">website</a>, <a href="https://doi.org/10.5281/zenodo.12664477">data</a>: UNFCCC (2024e), Pflüger and Gütschow (2024), <a href="https://github.com/primap-community/FAOSTAT_data_primap/">github</a></li> <li><strong>UNFCCC Biannial Update Reports, National Communications, and National Inventory </strong><br><strong>Reports for developing countries</strong> <a href="https://unfccc.int/BURs">website-BURs</a>, <a href="https://unfccc.int/non-annex-I-NCs">website-NCs</a>, <a href="https://github.com/JGuetschow/UNFCCC_non-AnnexI_data/tree/main/extracted_data/UNFCCC">data</a>: UNFCCC (2024d), UNFCCC (2024b). <ul> <li>Notes: <ol> <li>Not all BUR and NC submissions are included as reading the data is time consuming and not all submission contain sufficient data to be used in PRIMAP-hist.</li> <li>Not all submissions included in PRIMAP-hist are available in the github repository as we do not (yet) have code that we can publish for all submissions.</li> <li>for a list of added submissions see section "Data source updates (v2.7)" in the chnagelogin the pdf data description.</li> </ol> </li> </ul> </li> <li><strong>UNFCCC First Biannial Transparency Reports</strong> <a href="https://unfccc.int/first-biennial-transparency-reports">website</a>, [<a href="https://github.com/JGuetschow/UNFCCC_non-AnnexI_data/tree/main/extracted_data/UNFCCC">data</a>] UNFCCC (2025) <ul> <li>Notes: <ol> <li>For a list of added submissions see section "Data source updates (v2.7)" in the changelog in the pdf data description. </li> </ol> </li> </ul> </li> <li><strong>UNFCCC National Inventory Submissions 2025 (CRTAI)</strong> <a href="https://unfccc.int/ghg-inventories-annex-i-parties/2025">website</a>, <a href="https://doi.org/10.5194/essd-10-1427-2018">paper</a>, <a href="https://github.com/JGuetschow/UNFCCC_non-AnnexI_data/tree/main/extracted_data/UNFCCC">data</a>: UNFCCC (2024c) (processed based on Jeffery et al. (2018))</li> <li><strong>Official country repositories (non-UNFCCC)</strong> <ul> <li><strong>Belarus</strong>: Greenhouse gas statistics (1990-2022) <a href="https://www.belstat.gov.by/ofitsialnaya-statistika/makroekonomika-i-okruzhayushchaya-sreda/okruzhayuschaya-sreda/sovmestnaya-sistema-ekologicheskoi-informatsii2/b-izmenenie-klimata/b-3-vybrosy-parnikovyh-gazov/?_x_tr_sl=zh-TW&_x_tr_tl=en&_x_tr_hl=en-US&_x_tr_pto=wapp">website</a>: National Statistical Committee of the<br>Republic of Belarus (2024)</li> <li><strong>Mexico</strong>: 2023 Inventory <a href="https://historico.datos.gob.mx/busca/dataset/inventario-nacional-de-emisiones-de-gases-y-compuestos-de-efecto-invernadero-inegycei">website</a>,<a href="https://github.com/JGuetschow/UNFCCC_non-AnnexI_data/tree/master/extracted_data/non-UNFCCC/Mexico"> data</a>: Instituto Nacional de Ecologia y Gambio Climatico (2025)</li> <li><strong>South Korea</strong>: 2024 Inventory <a href="https://www.gir.go.kr/home/index.do?menuId=36">website</a>, <a href="https://github.com/JGuetschow/UNFCCC_non-AnnexI_data/tree/master/extracted_data/non-UNFCCC/Republic_of_Korea">data</a>: Republic of Korea (2024)</li> <li><strong>Taiwan / Republic of China</strong>: 2024 Inventory <a href="https://www.cca.gov.tw/en/information-service/publications/national-ghg-inventory-report/12003.html">website</a>, <a href="https://github.com/JGuetschow/UNFCCC_non-AnnexI_data/tree/master/extracted_data/non-UNFCCC/Taiwan">data</a>: Republic of China - Environmental<br>Protection Administration (2024)</li> <li><strong>Pakistan</strong>: 2016 inventory report <a href="https://www.gcisc.org.pk/GHGINVENTORY2011-2012_FINAL_GCISCRR19.pdf">website</a>, <a href="https://github.com/JGuetschow/UNFCCC_non-AnnexI_data/tree/master/extracted_data/non-UNFCCC/Pakistan">data</a>: Mir and Ijaz (2016)</li> <li><strong>Philippines</strong>: NICCDIES National GHG Inventory <a href="https://niccdies.climate.gov.ph/ghg-inventory/national">website</a>, <a href="https://github.com/JGuetschow/UNFCCC_non-AnnexI_data/tree/master/extracted_data/non-UNFCCC/Philippines">data</a>: NICCDIES (2025)</li> <li><strong>USA</strong>: Draft 2025 National Inventory <a href="https://www.edf.org/freedom-information-act-documents-epas-greenhouse-gas-inventory">website</a>, <a href="https://github.com/JGuetschow/UNFCCC_non-AnnexI_data/tree/master/extracted_data/non-UNFCCC/United_States_of_America">data</a>: US EPA (2025)</li> </ul> </li> </ul> <p>For the pre-1990 LULUCF time-series we use the following additional data sources:</p> <ul> <li><strong>Houghton land use CO2</strong> <a href="http://cdiac.ornl.gov/trends/landuse/houghton/houghton.html">website</a>: Houghton (2008)</li> <li><strong>HYDE land cover data</strong> <a href="http://themasites.pbl.nl/tridion/en/themasites/hyde/download/index-2.html">website</a>: Klein Goldewijk et al. (2010), Klein Goldewijk et al. (2011)</li> <li><strong>SAGE Global Potential Vegetation Dataset</strong> <a href="https://nelson.wisc.edu/sage/data-and-models/global-potential-vegetation/index.php">website</a>: Ramankutty and Foley (1999)</li> <li><strong>FAO Country Boundaries</strong> <a href="http://ref.data.fao.org/map?entryId=18329470-472d-11db-88e0-000d939bc5d8">website</a>: Food and Agriculture Organization of the United Nations<br>(2015)</li> </ul> <p> </p> <p><strong>Files included in the dataset</strong></p> <p>For each dataset we have three files: the <em>.nc</em> file contains the data and metadata in the native PRIMAP2 netCDF based format. The <em>.csv</em> file contains the data in a csv format following the specifications of the PRIMAP2 interchange format. The metadata for the interchange format file is included in the <em>.yaml</em> file.</p> <ul> <li><strong>Guetschow-et-al-2025a-PRIMAP-hist_v2.7_final_22-Aug-2025.X</strong>: The main dataset with numerical extrapolation of all time series to 2024 and three significant digits.</li> <li><strong>Guetschow-et-al-2025a-PRIMAP-hist_v2.7_final_no_extrap_22-Aug-2025.X</strong>: Variant without numerical extrapolation of missing values and not including the country groups mentioned in section ["area (ISO3)"] (three significant digits).</li> <li><strong>Guetschow-et-al-2025a-PRIMAP-hist_v2.7_final_no_rounding_22-Aug-2025.X</strong>: The main dataset with numerical extrapolation of all time series to 2024 and eleven significant digits.</li> <li><strong>Guetschow-et-al-2025a-PRIMAP-hist_v2.7_final_no_extrap_no_rounding_22-Aug-2025.X</strong>: Variant without numerical extrapolation of missing values and not including the country groups mentioned in section ["area (ISO3)"] (eleven significant digits).</li> <li><strong>PRIMAP-hist_v2.7_data-description.pdf</strong>: Data description including changelog.</li> <li><strong>PRIMAP-hist_v2.7_updated_figures.pdf</strong>: Updated figures from the PRIMAP-hist paper published in ESSD.</li> <li><strong>changes_PRIMAP-hist_v2.7_final_nr_to_v2.6_final_nr.xlsx: </strong>xlsx file which lists relative changes per country, gas and main sector for 2005, 2015, 2020, 2022, 2023, and 1990-2023 (cumulative) from v2.6 to v2.7 (comparison between current version and October 2024 version).</li> <li><strong>changes_PRIMAP-hist_v2.7_final_nr_to_v2.6.1_final_nr.xlsx</strong>: xlsx file which lists relative changes per country, gas and main sector for 2005, 2015, 2020, 2022, 2023, and 1990-2023 (cumulative) from v2.6.1 to v2.7 (comparison between latest version and current version).</li> <li><strong>changes_PRIMAP-hist_v2.7_final_nr_CR_to_TP.xlsx</strong>: xlsx file which lists relative differences per country, gas and main sector for 2005, 2015, 2020, 2023, 2024, and 1990-2024 (cumulative) between country reported priority and third party priority scenarios.</li> <li><strong>changelog_v2.6_final_to_v2.6.1_final.zip</strong>: Detailed changelog in html format containing plots and a breakdown of changes to sectors and gases for all countries. Country specific notes ar also included. To use the changelog locally unpack the zip file and open the file in the "html" directory in your browser. The changelog is also available on the <a href="https://primap.org/primap-hist/">primap.org</a> website.</li> </ul> <p><strong>Notes</strong></p> <ul> <li>Emissions from international aviation and shipping are not included in the dataset.</li> <li>Emissions from Land Use, Land-Use Change, and Forestry (LULUCF) are not included in the main version of this dataset. They are included in the version without rounding as users need to take extra care when using LULUCF data because changes some of the year-to-year changes in the data come from using different sources or methodology changes within a source rather than changes in actual emissions.</li> </ul> <p><strong>Data format description</strong></p> <p>The PRIMAP-hist data in the comma-separated values (CSV) files is formatted consistently with the PRIMAP2 interchange format.</p> <p>The data contained in each column are as follows:</p> <p><em>"source"</em></p> <p>Name of the data source. Here: <em>PRIMAP-hist_v2.7</em> with suffixes <em>_ne</em> for no extrapolation and <em>_nr</em> for no rounding.</p> <p><em>"scenario (PRIMAP)"</em></p> <ul> <li>HISTCR: In this scenario country-reported data (CRT, BUR / NIR / NC / UNFCCCDI) are prioritized over third-party data (CDIAC, FAO, Andrew, EDGAR, BP).</li> <li>HISTTP: In this scenario third-party data (CDIAC, FAO, Andrew, EDGAR, BP) are prioritized over country-reported data (CRT, BUR / NIR / NC / UNFCCCDI)</li> </ul> <p><em>"provenance"</em></p> <p>Provenance of the data. Here: "derived" as it is a composite source.</p> <p><em>"country"</em></p> <p>ISO 3166 three-letter country codes or custom codes for groups:</p> <table> <tbody> <tr> <td><strong>Code</strong></td> <td><strong>Region description</strong></td> </tr> <tr> <td>EARTH</td> <td>Aggregated emissions for all countries.</td> </tr> <tr> <td>ANNEXI</td> <td>Annex I Parties to the Convention</td> </tr> <tr> <td>NONANNEXI</td> <td>Non-Annex I Parties to the Convention</td> </tr> <tr> <td>AOSIS</td> <td>Alliance of Small Island States</td> </tr> <tr> <td>BASIC</td> <td>BASIC countries (Brazil, South Africa, India and China)</td> </tr> <tr> <td>EU27BX</td> <td>European Union post Brexit</td> </tr> <tr> <td>LDC</td> <td>Least Developed Countries</td> </tr> <tr> <td>UMBRELLA</td> <td>Umbrella Group</td> </tr> </tbody> </table> <p>Table: Additional "country" codes.</p> <p><em>"category"</em></p> <p>IPCC (Intergovernmental Panel on Climate Change) 2006 categories for emissions. Some aggregate sectors have been added to the hierarchy. These begin with the prefix <em>M</em>.</p> <table> <tbody> <tr> <td><strong>Code</strong></td> <td><strong>Description</strong></td> <td><strong>Gases covered</strong></td> </tr> <tr> <td>M.0.EL</td> <td>National Total excluding LULUCF</td> <td>all</td> </tr> <tr> <td>1</td> <td>Energy</td> <td>CO2, CH4, N2O</td> </tr> <tr> <td>1.A</td> <td>Fuel Combustion Activities</td> <td>CO2, CH4, N2O</td> </tr> <tr> <td>1.B</td> <td>Fugitive Emissions from Fuels</td> <td>CO2, CH4, N2O</td> </tr> <tr> <td>1.B.1</td> <td>Solid Fuels</td> <td>CO2, CH4, N2O</td> </tr> <tr> <td>1.B.2</td> <td>Oil and Natural Gas</td> <td>CO2, CH4, N2O</td> </tr> <tr> <td>1.B.3</td> <td>Other Emissions from Energy Production</td> <td>CO2, CH4, N2O</td> </tr> <tr> <td>1.C</td> <td>Carbon Dioxide Transport and Storage</td> <td>CO2</td> </tr> <tr> <td>2</td> <td>Industrial Processes and Product Use (IPPU)</td> <td>CO2, CH4, N2O, f-gases</td> </tr> <tr> <td>2.A</td> <td>Mineral Industry</td> <td>CO2</td> </tr> <tr> <td>2.B</td> <td>Chemical Industry</td> <td>CO2, CH4, N2O</td> </tr> <tr> <td>2.C</td> <td>Metal Industry</td> <td>CO2, CH4, N2O</td> </tr> <tr> <td>2.D</td> <td>Non-Energy Prod. from Fuels and Solvent Use</td> <td>CO2, CH4, N2O</td> </tr> <tr> <td>2.E</td> <td>Electronics Industry</td> <td>N2O (f-gases emitted but not resolved)</td> </tr> <tr> <td>2.F</td> <td> <p>Product uses as Substitutes for Ozone Depleting Substances </p> <p>(no data available as the category is only used for fluorinated gases which are only resolved at the level of category 2)</p> </td> <td>--</td> </tr> <tr> <td>2.G</td> <td>Other Product Manufacture and Use</td> <td>CO2, CH4, N2O</td> </tr> <tr> <td>2.H</td> <td>Other</td> <td>CO2, CH4, N2O</td> </tr> <tr> <td>M.AG</td> <td>Agriculture, sum of 3.A and M.AG.ELV</td> <td>CO2, CH4, N2O</td> </tr> <tr> <td>3.A</td> <td>Livestock</td> <td>CH4, N2O</td> </tr> <tr> <td>M.AG.ELV</td> <td>Agriculture excluding Livestock</td> <td>CO2, CH4, N2O</td> </tr> <tr> <td>4</td> <td>Waste</td> <td>CO2, CH4, N2O</td> </tr> <tr> <td>5</td> <td>Other</td> <td>CO2, CH4, N2O</td> </tr> </tbody> </table> <p>Table: Category descriptions using IPCC 2006 terminology.</p> <p>The categories are organized in a hierarchy where children of each category sum to the parent category. The full IPCC2006 category hierarchy contains many more subcategories, but data availability is not sufficient to resolve the full IPCC category tree in PRIMAP-hist. However, we will regularly assess if the available data allows for resolving addition subcategories. The categories currently resoved by PRIMAP-hist are organized as follows.<br><br> M.0.EL Total emissions excluding LULUCF<br> ├1 Energy<br> │├1.A Fuel Combustion Activities <br> │├1.B Fugitive Emissions from Fuels<br> ││├1.B.1 Solid Fuels<br> ││├1.B.2 Oil and Natural Gas<br> ││╰1.B.3 Other Emissions from Energy Production<br> │╰1.C Carbon Dioxide Transport and Storage<br> ├2 Industrial Processes and Product Use<br> │├2.A Mineral Industry<br> │├2.B Chemical Industry<br> │├2.C Metal Industry<br> │├2.D Non-Energy Products from Fuels and Solvent Use<br> │├2.E Electronics Industry<br> │├2.F Product Uses as Substitutes for Ozone Depleting Substances<br> │├2.G Other Product Manufacture and Use<br> │╰2.H Other<br> ├4 Waste<br> ├5 Other<br> ╰M.AG Agriculture<br> ├3.A Livestock<br> ╰M.AG.ELV Agriculture excluding Livestock<br><br><br>The "no_rounding" version of the dataset additionally contains data for the "Land Use, Land Use Change, and Forestry" sector and sectors sums including this sectors</p> <table> <tbody> <tr> <td><strong>Code</strong></td> <td><strong>Description</strong></td> <td><strong>Gases covered</strong></td> </tr> <tr> <td>0</td> <td>National Total including LULUCF</td> <td>CO2, CH4, N2O, f-gases</td> </tr> <tr> <td>3</td> <td>Agriculture, Forestry and Other Land Use (AFOLU)</td> <td>CO2, CH4, N2O</td> </tr> <tr> <td>M.LULUCF</td> <td>Land Use, Land Use Change, and Forestry</td> <td>CO2, CH4, N2O</td> </tr> </tbody> </table> <p>Table: Additional category descriptions using IPCC 2006 terminology.<br><br>PRIMAP-hist uses a distinction between agriculture and Land Use, Land Use Change and Forestry which are combined into the AFOLU sector in IPCC2006 categories. Thus, in contrast to the standard IPCC 2006 categories, category 3 "Agriculture, Forestry, and Other Land Use" is subdivided as follows:<br><br> 3 Agriculture, Forestry, and Other Land Use<br> ├M.AG Agriculture<br> │├3.A Livestock<br> │╰M.AG.ELV Agriculture excluding Livestock<br> ╰M.LULUCF Land Use, Land Use Change, and Forestry<br><br>Where a national total including LULUCF is reported, it is calculated as the sum of "M.0.EL Total emissions excluding LULUCF" and "M.LULUCF Land Use, Land Use Change, and Forestry":<br><br> 0 National Total<br> ├M.0.EL Total emissions excluding LULUCF<br> │├1 Energy<br> │├2 Industrial Processes and Product Use<br> │├4 Waste<br> │├5 Other<br> │╰M.AG Agriculture<br> ╰M.LULUCF Land Use, Land Use Change, and Forestry<br><br><br>"<em>entity</em>"<br><br>Gas baskets using global warming potentials (GWP) from the Second Assessment Report (SAR), Fourth Assessment Report (AR4), Fifth Assessment Report (AR5), Sixth Assessment Report (AR6).</p> <table> <tbody> <tr> <td><strong>Code</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>CH4</td> <td>Methane</td> </tr> <tr> <td>CO2</td> <td>Carbon Dioxide</td> </tr> <tr> <td>N2O</td> <td>Nitrous Oxide</td> </tr> <tr> <td>HFCS (SARGWP100)</td> <td>Hydrofluorocarbons (SAR)</td> </tr> <tr> <td>HFCS (AR4GWP100)</td> <td>Hydrofluorocarbons (AR4)</td> </tr> <tr> <td>HFCS (AR5GWP100)</td> <td>Hydrofluorocarbons (AR5)</td> </tr> <tr> <td>HFCS (AR6GWP100)</td> <td>Hydrofluorocarbons (AR6)</td> </tr> <tr> <td>PFCS (SARGWP100)</td> <td>Perfluorocarbons (SAR)</td> </tr> <tr> <td>PFCS (AR4GWP100)</td> <td>Perfluorocarbons (AR4)</td> </tr> <tr> <td>PFCS (AR5GWP100)</td> <td>Perfluorocarbons (AR5)</td> </tr> <tr> <td>PFCS (AR6GWP100)</td> <td>Perfluorocarbons (AR6)</td> </tr> <tr> <td>SF6</td> <td>Sulfur Hexafluoride</td> </tr> <tr> <td>NF3</td> <td>Nitrogen Trifluoride</td> </tr> <tr> <td>FGASES (SARGWP100)</td> <td>Fluorinated Gases (SAR): HFCs, PFCs, SF6, NF3</td> </tr> <tr> <td>FGASES (AR4GWP100)</td> <td>Fluorinated Gases (AR4): HFCs, PFCs, SF6, NF3</td> </tr> <tr> <td>FGASES (AR5GWP100)</td> <td>Fluorinated Gases (AR5): HFCs, PFCs, SF6, NF3</td> </tr> <tr> <td>FGASES (AR6GWP100)</td> <td>Fluorinated Gases (AR6): HFCs, PFCs, SF6, NF3</td> </tr> <tr> <td>KYOTOGHG (SARGWP100)</td> <td>Kyoto greenhouse gases (SAR)</td> </tr> <tr> <td>KYOTOGHG (AR4GWP100)</td> <td>Kyoto greenhouse gases (AR4)</td> </tr> <tr> <td>KYOTOGHG (AR5GWP100)</td> <td>Kyoto greenhouse gases (AR5)</td> </tr> <tr> <td>KYOTOGHG (AR6GWP100)</td> <td>Kyoto greenhouse gases (AR6)</td> </tr> </tbody> </table> <p>Table: Gas categories and underlying global warming potentials</p> <p><em>"unit"</em></p> <p>Unit is either <em><substance> * gigagram / a</em> where substance is the entity or for CO2 equivalent units <em>CO2 * gigagram / a</em>. The CO2-equivalent is calculated according to the global warming potential indicated by the entity (see above).</p> <p><em>Remaining columns</em></p> <p>Years from 1750-2024.</p> <p><strong>Changelog</strong></p> <p>For the changelog we refer to the pdf version of the data description available for download here</p> <p><strong>References</strong></p> <p>For the full references we refer to the references section and the pdf version of the data description available for download here.</p>
Raw time series of GNSS sites used in the manuscript 'South China plate motion modified by 2008 Mw7.9 great Wenchuan earthquake'
<p>The *.pre.*.neu files are raw position time series before the Wenchuan earthquake (from July 2001 to December 2004) and the *.aft.*.neu files are the raw position time series after the Wenchuan earthquake (from July 2014 to December 2017). The file 'breaks' contains the epoches of breaks due to equipment changes or that are indentified visually.</p>
Raw time series of GNSS sites used in the manuscript 'South China plate motion modified by 2008 Mw7.9 great Wenchuan earthquake'
<p>The *.pre.*.neu files are raw position time series before the Wenchuan earthquake (from July 2001 to December 2004) and the *.aft.*.neu files are the raw position time series after the Wenchuan earthquake (from July 2014 to December 2017). The file 'breaks' contains the epochs of breaks due to equipment changes or that are identified visually.</p>
Mean wind and stability time series at FINO1 between June and July 2015 (from OBLEX-F1)
<p>Data collected from a sonic anemometer mounted at 15m above the mean sea level at the geographical location of FINO1 offshore met-mast. Time series cover yeardays between 160 and 210, 2015.</p> <p>% % Datasets are as follows:</p> <p>% % time15: mean time at each 30-min burst of 15m sonic data.</p> <p>% % umean15: mean wind speed at 15m height from average of each sonic 30-min segment at 15m sonic.</p> <p>% % UU10: wind at 10m heigh from umean15 using logarithmic wind profile.</p> <p>% % ti_ref: reference time - first of January 2015.</p> <p>% % wdir: wind direction at 15m measured from high freq. sonic data.</p> <p>% % wa: wave age data estimated from sonic data and wave phase speed (see OBLEX-F1 data).</p> <p>% % L15: Moninnon-Obukhov length scale measured by 25Hz sonic data at 15m height.</p> <p>% %</p> <p>% % Mostafa Bakhoday Paskyabi, 10 Dec. 2022</p> <p>% % Mostafa.Bakhoday-Paskyabi@uib.no</p> <p> </p> <p> </p>
ScienceDex guides
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.