United States CES: Ave Age of Reference Person data was reported at 50.900 Year in 2017. This stayed constant from the previous number of 50.900 Year for 2016. United States CES: Ave Age of Reference Person data is updated yearly, averaging 48.100 Year from Dec 1984 (Median) to 2017, with 34 observations. The data reached an all-time high of 50.900 Year in 2017 and a record low of 46.700 Year in 1986. United States CES: Ave Age of Reference Person data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.H042: Consumer Expenditure Survey.

Chile Average Reference Price: Gasoline: Mean data was reported at 1,214.926 USD/Cub m in Mar 2025. This records a decrease from the previous number of 1,222.727 USD/Cub m for Feb 2025. Chile Average Reference Price: Gasoline: Mean data is updated monthly, averaging 530.403 USD/Cub m from Feb 1991 (Median) to Mar 2025, with 410 observations. The data reached an all-time high of 1,424.930 USD/Cub m in May 2024 and a record low of 156.000 USD/Cub m in Jan 2000. Chile Average Reference Price: Gasoline: Mean data remains active status in CEIC and is reported by National Commission of Energy. The data is categorized under Global Database’s Chile – Table CL.P003: Energy Products: Average Reference and Parity Price.

The COSPAR International Reference Atmosphere (CIRA) provides empirical models of atmospheric temperatures and densities as recommended by the Committee on Space Research (COSPAR). A global climatology of atmospheric temperature, zonal velocity and geopotential height derived from a combination of satellite, radiosonde and ground-based measurements and model data. The reference atmosphere extends from pole to pole and 0-120 km. The majority of the data are on a 5 degree latitude grid and approximately 2 km vertical resolution.

The lower part (0-120 km) of CIRA-86 consists of tables of the monthly mean values of temperature and zonal wind with almost global coverage (80N - 80S). Two sets of files were compiled, one in pressure coordinates including also the geopotential heights, and one in height coordinates including also the pressure values. These tables were generated from several global data compilations including ground-based and satellite (Nimbus 5,6,7) measurements: Oort (1983), Labitzke et al. (1985). The lower part was merged with MSIS-86 at 120 km altitude. In general, hydrostatic and thermal wind balance are maintained at all levels. The model accurately reproduces most of the characteristic features of the atmosphere such as the equatorial wind and the general structure of the tropopause, stratopause, and mesopause.

This data set is a corrected version of the original CIRA data files as provided by J. Barnett (July 1990) in ASCII format.

Additionally a netCDF version of the data were produced by BADC user and added to this dataset.

This dataset is public.

Global Population of the World (GPW) translates census population data to a latitude-longitude grid so that population data may be used in cross-disciplinary studies. There are three data files with this data set for the reference years 1990 and 1995. Over 127,000 administrative units and population counts were collected and integrated from various sources to create the gridded data. In brief, GPW was created using the following steps: * Population data were estimated for the product reference years, 1990 and 1995, either by the data source or by interpolating or extrapolating the given estimates for other years. * Additional population estimates were created by adjusting the source population data to match UN national population estimates for the reference years. * Borders and coastlines of the spatial data were matched to the Digital Chart of the World where appropriate and lakes from the Digital Chart of the World were added. * The resulting data were then transformed into grids of UN-adjusted and unadjusted population counts for the reference years. * Grids containing the area of administrative boundary data in each cell (net of lakes) were created and used with the count grids to produce population densities.As with any global data set based on multiple data sources, the spatial and attribute precision of GPW is variable. The level of detail and accuracy, both in time and space, vary among the countries for which data were obtained.

This data set under CC-BY license contains time series of total abundance and/or biomass of assemblages of insect, arachnid and Entognatha assemblages (grouped at the family level or higher taxonomic resolution), monitored by standardized means for ten or more years. The data were derived from 166 data sources, representing a total of 1676 sites from 41 countries. The time series for abundance and biomass represent the aggregated number of all individuals of all taxa monitored at each site. The data set consists of four linked tables, representing information on the study level, the plot level, about sampling, and the measured assemblage sizes. all references to the original data sources can be found in the pdf with references, and a Google Earth file (kml) file presents the locations (including metadata) of all datasets. When using (parts of) this data set, please respect the original open access licenses. This data set underlies all analyses performed in the paper 'Meta-analysis reveals declines in terrestrial, but increases in freshwater insect abundances', a meta-analysis of changes in insect assemblage sizes, and is accompanied by a data paper entitled 'InsectChange – a global database of temporal changes in insect and arachnid assemblages'. Consulting the data paper before use is recommended. Tables that can be used to calculate trends of specific taxa and for species richness will be added as they become available. The data set consists of four tables that are linked by the columns 'DataSource_ID'. and 'Plot_ID', and a table with references to original research. In the table 'DataSources', descriptive data is provided at the dataset level: Links are provided to online repositories where the original data can be found, it describes whether the dataset provides data on biomass, abundance or both, the invertebrate group under study, the realm, and describes the location of sampling at different geographic scales (continent to state). This table also contains a reference column. The full reference to the original data is found in the file 'References_to_original_data_sources.pdf'. In the table 'PlotData' more details on each site within each dataset are provided: there is data on the exact location of each plot, whether the plots were experimentally manipulated, and if there was any spatial grouping of sites (column 'Location'). Additionally, this table contains all explanatory variables used for analysis, e.g. climate change variables, land-use variables, protection status. The table 'SampleData' describes the exact source of the data (table X, figure X, etc), the extraction methods, as well as the sampling methods (derived from the original publications). This includes the sampling method, sampling area, sample size, and how the aggregation of samples was done, if reported. Also, any calculations we did on the original data (e.g. reverse log transformations) are detailed here, but more details are provided in the data paper. This table links to the table 'DataSources' by the column 'DataSource_ID'. Note that each datasource may contain multiple entries in the 'SampleData' table if the data were presented in different figures or tables, or if there was any other necessity to split information on sampling details. The table 'InsectAbundanceBiomassData' provides the insect abundance or biomass numbers as analysed in the paper. It contains columns matching to the tables 'DataSources' and 'PlotData', as well as year of sampling, a descriptor of the period within the year of sampling (this was used as a random effect), the unit in which the number is reported (abundance or biomass), and the estimated abundance or biomass. In the column for Number, missing data are included (NA). The years with missing data were added because this was essential for the analysis performed, and retained here because they are easier to remove than to add. Linking the table 'InsectAbundanceBiomassData.csv' with 'PlotData.csv' by column 'Plot_ID', and with 'DataSources.csv' by column 'DataSource_ID' will provide the full dataframe used for all analyses. Detailed explanations of all column headers and terms are available in the ReadMe file, and more details will be available in the forthcoming data paper. WARNING: Because of the disparate sampling methods and various spatial and temporal scales used to collect the original data, this dataset should never be used to test for differences in insect abundance/biomass among locations (i.e. differences in intercept). The data can only be used to study temporal trends, by testing for differences in slopes. The data are standardized within plots to allow the temporal comparison, but not necessarily among plots (even within one dataset).

Reference Barcode Generation from Fish Tissue Samples To generate a more complete 12S barcode reference database for California Current Large Marine Ecosystem fishes, we assembled a list of the 1,144 marine teleost and elasmobranch species that occur in this system (Allen & Horn, 2006; Froese & Pauly, 2010; Hastings & Burton, 2008; Love, & Passarelli, 2020) (Table S1). From this list, we acquired 741 ethanol-preserved voucher specimens representing 597 species (Table S1, Table S2) from the Scripps Institution of Oceanography Marine Vertebrate Collection at the University of California San Diego. DNA was extracted from each tissue sample using a Chelex 100 extraction method (Walsh, Metzger, & Higuchi, 1991), as described in the Supplemental Methods. We amplified all teleost DNA extracts (n=701) using the MiFish Universal Teleost Primers (Miya et al., 2015), and all elasmobranchs (n=55) using the MiFish Elasmobranch primers (Miya et al., 2015) following the thermocycler profile of Curd et al., (2019) (Table S3). We Sanger sequenced purified amplicons (see Supplemental Methods for details), and aligned and trimmed forward and reverse sequences in Sequencher version 5.4.6 (Nishimura, 2000). We used R package taxize (version 0.9.99) (Chamberlain & Szöcs, 2013) to synonymize taxonomic names of all vouchered specimens and GenBank. We then checked the accuracy of generated reference barcodes by building a UPGMA phylogenetic tree of all reference sequences and California Current Large Marine Ecosystem fishes using phangorn (2.5.5). In addition, we queried each sequence using blastn (Camacho et al., 2009) and removed any sequence that did not cluster or align to known taxonomic lineages (data available at https://doi.org/10.5068/D1H963). The resulting 12S reference barcodes were deposited into GenBank (SAMN19289093–SAMN19289810; Table S2). Reference Database Creation To test variation in taxonomic assignment among reference databases, we generated three distinct reference sequence databases: “CRUX-GenBank”, “global”, and “regional” (Table 1 and Table 2). CRUX-GenBank is a custom 12S reference database generated using Creating Reference libraries Using eXisting tools (CRUX) module of the Anacapa Toolkit to query GenBank for reference barcodes conducted with standard search parameters (Benson et al., 2018; Curd et al., 2019) and MiFish Universal 12S sequences (Table S1) as the user‐defined primers. Briefly, we created this reference database by running in silico PCR (Ficetola et al., 2010) on the European Molecular Biology Laboratory (EMBL) standard nucleotide database (Stoesser et al., 2002) to generate a seed library of 12S references. Next, we used blastn (Camacho et al., 2009) to capture reference barcodes without included primer sequences and to query the seed database against the NCBI non‐redundant nucleotide database (Gold, 2020; Pruitt et al., 2005; sequences downloaded in October 2019). The resulting blastn hits were de‐replicated by retaining only the longest version of each sequence and taxonomy for each accession was retrieved using Entrez‐qiime (Baker, 2016). The resulting set of reference sequences in the CRUX-GenBank database included any GenBank reference barcodes that in silico amplified to the MiFish 12S primers at the time of this analysis. We created the global database to evaluate whether increasing database completeness improves taxonomic assignment. To create the global database, we supplemented the CRUX-GenBank database with 741 additional California Current Large Marine Ecosystem fish 12S barcodes generated for this study (Table S2). Thus, the global database includes all fish 12S reference sequences available at the time of download. From this global database, we created the regional database, including only 12S sequences of fishes known to occur in the California Current Large Marine Ecosystem. We created this database to specifically test whether databases curated to specific ecosystems enhance taxonomic assignment performance relative to more comprehensive databases (“global”). Because of the high degree of similarity between the MiFish Universal and Elasmobranch loci and the flexibility built into CRUX, a single CRUX generated 12S reference database performs well for both markers (Curd et al., 2019), so we did not create separate teleost and elasmobranch databases. Additionally, because the MiFish primer set amplifies nearly all vertebrate taxa (Miya et al., 2015; Valsecchi et al., 2019), the global database include teleosts, elasmobranchs, mammals, reptiles, amphibians, birds, etc. All databases are available at https://doi.org/10.5068/D1H963. Taxonomy cross-validation by identity comparisons We implemented the taxonomy cross-validation by identity (TAXXI) framework developed by (Edgar, 2018a) to 1) compare taxonomic assignment performance metrics for global versus regional reference databases...

The Crustal Dynamics Data Information System (CDDIS) supports the space geodesy and geodynamics community through NASA's Space Geodesy Project as well as NASA's Earth Science Enterprise. The CDDIS was established in 1982 at NASA's Goddard Space Flight Center as a dedicated data bank to archive and distribute space geodesy related data sets. Today, the CDDIS archives and distributes mainly Global Navigation Satellite Systems (GNSS, currently Global Positioning System GPS and GLObal NAvigation Satellite System GLONASS), laser ranging (both to artificial satellites, SLR, and lunar, LLR), Very Long Baseline Interferometry (VLBI), and Doppler Orbitography and Radio-positioning Integrated by Satellite (DORIS) data for an ever increasing user community of geophysicists. The CDDIS serves as a global data center for the International GNSS Service (IGS) since 1992, the International Laser Ranging Service (ILRS), the International VLBI Service for Geodesy and Astrometry (IVS), International DORIS Service (IDS), and the International Earth Rotation and Reference Systems Service (IERS). General information, including summary reports, data set documentation, etc., are available through the CDDIS archive.

Ukraine Spread Between Reference Lending and Deposit Rates (Base Points) data was reported at 658.000 % in Mar 2018. This records a decrease from the previous number of 681.000 % for Dec 2017. Ukraine Spread Between Reference Lending and Deposit Rates (Base Points) data is updated quarterly, averaging 583.500 % from Dec 2005 (Median) to Mar 2018, with 50 observations. The data reached an all-time high of 892.000 % in Mar 2009 and a record low of 354.000 % in Jun 2014. Ukraine Spread Between Reference Lending and Deposit Rates (Base Points) data remains active status in CEIC and is reported by National Bank of Ukraine. The data is categorized under Global Database’s Ukraine – Table UA.KB031: Financial Soundness Indicators.

The International GPS Service for Geodynamics (IGS) supports researchers who use satellites in the U.S. Defense Department's Global Positioning System (GPS) for studies in geodynamics. GPS satellites transmit signals that allow one to determine, with great accuracy, the locations of GPS receivers. The receivers can be fixed on the Earth, in moving vehicles, aircraft, or in low-Earth orbiting satellites. The time evolution of receiver locations allows researchers to study the motions of tectonic plates, displacements associated with earthquakes, earth orientation, and other geophysically interesting phenomena. Global Positioning System (GPS) uses microwave measurements of range (transmitter-to-receiver distance), accurate to within a centimeter or less, to determine positions of stations on the Earth's surface. Relative site positions can be measured to within a centimeter. This system was developed by the U.S. Department of Defense for military navigation and timing.

The INTERNATIONAL GPS SERVICE FOR GEODYNAMICS (IGS), a service established by the International Association of Geodesy (IAG), officially started its activities on January 1, 1994, after a successful pilot phase of more than one year. IGS is based on about 40 globally distributed permanent GPS tracking sites, three Global Data Centers, five Operational or Regional Data Centers, seven Analysis Centers, and a Central Bureau. Some fifty institutions and organizations contribute to these activities.

IGS routinely provides :

-High-quality orbits for all GPS satellites (estimated accuracy better than 20 cm).

-Earth Rotation Parameters

-Contributions to the determination of the tracking site coordinates in the International Terrestrial Reference Frame (ITRF), in cooperation with the International Earth Rotation Service (IERS).

-Phase and pseudorange observations in daily RINEX files for each IGS tracking site.

Institut Geographique National (IGN), France, is a Global Data Center of IGS. It is accessible by FTP at "ftp://igs.ensg.ign.fr/pub/igs/products/".

The following data is available from the IGN Data Center :

Data is kept available on-line for 150 days. Access may be made available to off-line data on special request. The data would be on 9-inch tape, optical disk, or exabyte tape. Questions may be directed to the IGS Central Bureau as well as to this data center.

Chile Average Reference Price: Diesel: Mean data was reported at 1,338.120 USD/Cub m in Mar 2025. This records a decrease from the previous number of 1,342.159 USD/Cub m for Feb 2025. Chile Average Reference Price: Diesel: Mean data is updated monthly, averaging 525.726 USD/Cub m from Feb 1991 (Median) to Mar 2025, with 410 observations. The data reached an all-time high of 1,802.719 USD/Cub m in Jan 2023 and a record low of 140.000 USD/Cub m in Jan 2000. Chile Average Reference Price: Diesel: Mean data remains active status in CEIC and is reported by National Commission of Energy. The data is categorized under Global Database’s Chile – Table CL.P003: Energy Products: Average Reference and Parity Price.

Japan The Reference Dates of Business Cycle: By Month data was reported at 0.000 Unit in Apr 2025. This stayed constant from the previous number of 0.000 Unit for Mar 2025. Japan The Reference Dates of Business Cycle: By Month data is updated monthly, averaging 0.000 Unit from Jun 1951 (Median) to Apr 2025, with 887 observations. The data reached an all-time high of 1.000 Unit in May 2020 and a record low of 0.000 Unit in Apr 2025. Japan The Reference Dates of Business Cycle: By Month data remains active status in CEIC and is reported by Economic and Social Research Institute. The data is categorized under Global Database’s Japan – Table JP.S095: Business-Cycle Dating. An interpretation of US Business Cycle Expansions and Contractions data provided by The National Bureau of Economic Research (NBER). A value of 1 is a recessionary period, while a value of 0 is an expansionary period.

Chile Average Reference Price: Domestic Kerosene: Mean data was reported at 707.575 USD/Cub m in Mar 2025. This records a decrease from the previous number of 731.225 USD/Cub m for Feb 2025. Chile Average Reference Price: Domestic Kerosene: Mean data is updated monthly, averaging 416.137 USD/Cub m from Feb 1991 (Median) to Mar 2025, with 410 observations. The data reached an all-time high of 1,002.275 USD/Cub m in Apr 2024 and a record low of 148.000 USD/Cub m in Jan 2000. Chile Average Reference Price: Domestic Kerosene: Mean data remains active status in CEIC and is reported by National Commission of Energy. The data is categorized under Global Database’s Chile – Table CL.P003: Energy Products: Average Reference and Parity Price.

Chile Average Reference Price: Gasoline: High data was reported at 1,275.673 USD/Cub m in Mar 2025. This records a decrease from the previous number of 1,283.863 USD/Cub m for Feb 2025. Chile Average Reference Price: Gasoline: High data is updated monthly, averaging 556.904 USD/Cub m from Feb 1991 (Median) to Mar 2025, with 410 observations. The data reached an all-time high of 1,496.177 USD/Cub m in May 2024 and a record low of 158.000 USD/Cub m in Nov 1991. Chile Average Reference Price: Gasoline: High data remains active status in CEIC and is reported by National Commission of Energy. The data is categorized under Global Database’s Chile – Table CL.P003: Energy Products: Average Reference and Parity Price.

Policy Rate: Month End: Reference Interest Rate data was reported at 2.970 % pa in Apr 2025. This records a decrease from the previous number of 3.060 % pa for Mar 2025. Policy Rate: Month End: Reference Interest Rate data is updated monthly, averaging 2.805 % pa from Jan 2013 (Median) to Apr 2025, with 148 observations. The data reached an all-time high of 4.020 % pa in Aug 2014 and a record low of 0.900 % pa in Jan 2013. Policy Rate: Month End: Reference Interest Rate data remains active status in CEIC and is reported by Central Bank of Bolivia. The data is categorized under Global Database’s Bolivia – Table BO.M001: Policy Rate.

Sweden Repo Rate: Riksbank: Minimum data was reported at -0.500 % pa in Oct 2018. This stayed constant from the previous number of -0.500 % pa for Sep 2018. Sweden Repo Rate: Riksbank: Minimum data is updated monthly, averaging 2.250 % pa from Jun 1994 (Median) to Oct 2018, with 293 observations. The data reached an all-time high of 8.910 % pa in Dec 1995 and a record low of -0.500 % pa in Oct 2018. Sweden Repo Rate: Riksbank: Minimum data remains active status in CEIC and is reported by The Riksbank. The data is categorized under Global Database’s Sweden – Table SE.M016: Repo Rate and Reference Rate.

Brazil BR: Average Reference Price: Natural Gas data was reported at 892.130 BRL/1000 Cub m in 2023. This records a decrease from the previous number of 1,491.078 BRL/1000 Cub m for 2022. Brazil BR: Average Reference Price: Natural Gas data is updated yearly, averaging 490.434 BRL/1000 Cub m from Dec 2002 (Median) to 2023, with 22 observations. The data reached an all-time high of 1,491.078 BRL/1000 Cub m in 2022 and a record low of 200.000 BRL/1000 Cub m in 2002. Brazil BR: Average Reference Price: Natural Gas data remains active status in CEIC and is reported by National Petroleum, Natural Gas and Biofuels Agency. The data is categorized under Global Database’s Brazil – Table BR.PE003: Average Reference Price: Natural Gas.

Chile Average Reference Price: Domestic Kerosene: High data was reported at 796.000 USD/Cub m in Mar 2025. This records a decrease from the previous number of 822.650 USD/Cub m for Feb 2025. Chile Average Reference Price: Domestic Kerosene: High data is updated monthly, averaging 463.043 USD/Cub m from Feb 1991 (Median) to Mar 2025, with 410 observations. The data reached an all-time high of 1,127.575 USD/Cub m in Apr 2024 and a record low of 167.000 USD/Cub m in Jan 2000. Chile Average Reference Price: Domestic Kerosene: High data remains active status in CEIC and is reported by National Commission of Energy. The data is categorized under Global Database’s Chile – Table CL.P003: Energy Products: Average Reference and Parity Price.

Argentina Reference Interest Rate: Repo: 30 Days data was reported at 34.390 % pa in May 2021. This records an increase from the previous number of 33.990 % pa for Apr 2021. Argentina Reference Interest Rate: Repo: 30 Days data is updated monthly, averaging 14.790 % pa from Jul 2002 (Median) to May 2021, with 227 observations. The data reached an all-time high of 64.690 % pa in Aug 2019 and a record low of 1.550 % pa in Feb 2004. Argentina Reference Interest Rate: Repo: 30 Days data remains active status in CEIC and is reported by Central Bank of Argentina. The data is categorized under Global Database’s Argentina – Table AR.M001: Policy Rate.

Chile Average Reference Price: Liquefied Petroleum Gas: High data was reported at 716.735 USD/Cub m in Mar 2025. This records a decrease from the previous number of 750.895 USD/Cub m for Feb 2025. Chile Average Reference Price: Liquefied Petroleum Gas: High data is updated monthly, averaging 336.196 USD/Cub m from Feb 1991 (Median) to Mar 2025, with 410 observations. The data reached an all-time high of 910.484 USD/Cub m in Jul 2022 and a record low of 150.000 USD/Cub m in Jan 2000. Chile Average Reference Price: Liquefied Petroleum Gas: High data remains active status in CEIC and is reported by National Commission of Energy. The data is categorized under Global Database’s Chile – Table CL.P003: Energy Products: Average Reference and Parity Price.

China Energy Consumption per GDP: Crude Oil data was reported at 6.854 Ton/MN RMB in 2018. This records a decrease from the previous number of 7.079 Ton/MN RMB for 2017. China Energy Consumption per GDP: Crude Oil data is updated yearly, averaging 20.924 Ton/MN RMB from Dec 1980 (Median) to 2018, with 39 observations. The data reached an all-time high of 200.650 Ton/MN RMB in 1980 and a record low of 6.854 Ton/MN RMB in 2018. China Energy Consumption per GDP: Crude Oil data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under Global Database’s China – Table CN.RBC: Reference Data: Energy Consumption per GDP(Discontinued).