SPI natural origin spawner abundance metrics and indicators data

Algeria DZ: SPI: Pillar 4 Data Sources Score: Scale 0-100 data was reported at 45.958 NA in 2022. This records a decrease from the previous number of 49.075 NA for 2021. Algeria DZ: SPI: Pillar 4 Data Sources Score: Scale 0-100 data is updated yearly, averaging 49.892 NA from Dec 2016 (Median) to 2022, with 7 observations. The data reached an all-time high of 52.417 NA in 2018 and a record low of 45.958 NA in 2022. Algeria DZ: SPI: Pillar 4 Data Sources Score: Scale 0-100 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Algeria – Table DZ.World Bank.WDI: Governance: Policy and Institutions. The data sources overall score is a composity measure of whether countries have data available from the following sources: Censuses and surveys, administrative data, geospatial data, and private sector/citizen generated data. The data sources (input) pillar is segmented by four types of sources generated by (i) the statistical office (censuses and surveys), and sources accessed from elsewhere such as (ii) administrative data, (iii) geospatial data, and (iv) private sector data and citizen generated data. The appropriate balance between these source types will vary depending on a country’s institutional setting and the maturity of its statistical system. High scores should reflect the extent to which the sources being utilized enable the necessary statistical indicators to be generated. For example, a low score on environment statistics (in the data production pillar) may reflect a lack of use of (and low score for) geospatial data (in the data sources pillar). This type of linkage is inherent in the data cycle approach and can help highlight areas for investment required if country needs are to be met.;Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators);Weighted average;

United States US: Statistical Performance Indicators (SPI): Overall Score: Scale 0-100 data was reported at 93.430 NA in 2023. This records an increase from the previous number of 91.875 NA for 2022. United States US: Statistical Performance Indicators (SPI): Overall Score: Scale 0-100 data is updated yearly, averaging 87.614 NA from Dec 2016 (Median) to 2023, with 8 observations. The data reached an all-time high of 93.430 NA in 2023 and a record low of 86.898 NA in 2020. United States US: Statistical Performance Indicators (SPI): Overall Score: Scale 0-100 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Governance: Policy and Institutions. The SPI overall score is a composite score measuing country performance across five pillars: data use, data services, data products, data sources, and data infrastructure.;Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators);Weighted average;

5km gridded Standardised Precipitation Index (SPI) data for Great Britain, which is a drought index based on the probability of precipitation for a given accumulation period as defined by McKee et al [1]. There are seven accumulation periods: 1, 3, 6, 9, 12, 18, 24 months and for each period SPI is calculated for each of the twelve calendar months. Note that values in monthly (and for longer accumulation periods also annual) time series of the data therefore are likely to be autocorrelated. The standard period which was used to fit the gamma distribution is 1961-2010. The dataset covers the period from 1862 to 2015. This version supersedes previous versions (version 2 and 3) of the same dataset due to minor errors in the data files. NOTE: the difference between this dataset with the previously published dataset 'Gridded Standardized Precipitation Index (SPI) using gamma distribution with standard period 1961-2010 for Great Britain [SPIgamma61-10]' (Tanguy et al., 2015; https://doi.org/10.5285/94c9eaa3-a178-4de4-8905-dbfab03b69a0) , apart from the temporal and spatial extent, is the underlying rainfall data from which SPI was calculated. In the previously published dataset, CEH-GEAR (Tanguy et al., 2014; https://doi.org/10.5285/5dc179dc-f692-49ba-9326-a6893a503f6e) was used, whereas in this new version, Met Office 5km rainfall grids were used (see supporting information for more details). The methodology to calculate SPI is the same in the two datasets. [1] McKee, T. B., Doesken, N. J., Kleist, J. (1993). The Relationship of Drought Frequency and Duration to Time Scales. Eighth Conference on Applied Climatology, 17-22 January 1993, Anaheim, California.

This document presents the dataset of the definition of evaluation methods, detailed in the work "Reporting the Application of a Gamification Evaluation Framework to Solve Software Process Improvement Problems"

These files provide the data and codes that are used to construct the SPI.

Droughts are natural occurring events in which dry conditions persist over time. Droughts are complex to characterize because they depend on water and energy balances at different temporal and spatial scales. The Standardized Precipitation Index (SPI) is used to analyze meteorological droughts. SPI estimates the deviation of precipitation from the long-term probability function at different time scales (e.g. 1, 3, 6, 9, or 12 months). SPI only uses monthly precipitation as an input, which can be helpful for characterizing meteorological droughts. Other variables should be included (e.g. temperature or evapotranspiration) in the characterization of other types of droughts (e.g. agricultural droughts).This layer shows the SPI index at different temporal periods calculated using the SPEI library in R and precipitation data from CHIRPS data set.Sources:Climate Hazards Center InfraRed Precipitation with Station data (CHIRPS)SPEI R library

Lithuania LT: SPI: Pillar 5 Data Infrastructure Score: Scale 0-100 data was reported at 100.000 NA in 2019. This stayed constant from the previous number of 100.000 NA for 2018. Lithuania LT: SPI: Pillar 5 Data Infrastructure Score: Scale 0-100 data is updated yearly, averaging 100.000 NA from Dec 2016 (Median) to 2019, with 4 observations. The data reached an all-time high of 100.000 NA in 2019 and a record low of 100.000 NA in 2019. Lithuania LT: SPI: Pillar 5 Data Infrastructure Score: Scale 0-100 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Lithuania – Table LT.World Bank.WDI: Governance: Policy and Institutions. The data infrastructure pillar overall score measures the hard and soft infrastructure segments, itemizing essential cross cutting requirements for an effective statistical system. The segments are: (i) legislation and governance covering the existence of laws and a functioning institutional framework for the statistical system; (ii) standards and methods addressing compliance with recognized frameworks and concepts; (iii) skills including level of skills within the statistical system and among users (statistical literacy); (iv) partnerships reflecting the need for the statistical system to be inclusive and coherent; and (v) finance mobilized both domestically and from donors.;Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators);Weighted average;

Values of the Standardized Precipitation Index (SPI) over The Netherlands. The period is 6 months

This data set represents the sediment profile imaging data from 2002.In Fall/Winter 2002, researchers from the Virginia Institute of Marine Science (VIMS) and the NOAA Office for Coastal Management conducted a project to map benthic habitats by Catlett and Goodwin Islands on the York River, Chesapeake Bay, Virginia. Sediment grab samples were collected at 56 stations and sediment profile images were collected at 200 stations. Sampling areas were also surveyed using side scan sonar and interferometric swath bathymetry sensors. Scientists from the Virginia Institute of Marine Sciences returned to sample a subset of the 2002 sediment grab data in 2003 and then again in 2004. A subset of SPI stations (79) were revisited in 2004. Original contact information: Contact Org: NOAA Office for Coastal Management Phone: 843-740-1202 Email: coastal.info@noaa.gov

WDFW SPI wild salmonid abundance

1. The integration and synthesis of the data in different areas of science is drastically slowed and hindered by a lack of standards and networking programmes. Long-term studies of individually marked animals are not an exception. These studies are especially important as instrumental for understanding evolutionary and ecological processes in the wild. Further, their number and global distribution provides a unique opportunity to assess the generality of patterns and to address broad-scale global issues (e.g. climate change).

2. To solve data integration issues and enable a new scale of ecological and evolutionary research based on long-term studies of birds, we have created the SPI-Birds Network and Database (www.spibirds.org) – a large-scale initiative that connects data from, and researchers working on, studies of wild populations of individually recognizable (usually ringed) birds. Within year and a half since the establishment, SPI-Birds has recruited over 120 members, and currently hosts data on almost 1.5 million individual birds collected in 80 populations over 2000 cumulative years, and counting.

3. SPI-Birds acts as a data hub and a catalogue of studied populations. It prevents data loss, secures easy data finding, use and integration, and thus facilitates collaboration and synthesis. We provide community-derived data and meta-data standards and improve data integrity guided by the principles of Findable, Accessible, Interoperable, and Reusable (FAIR), and aligned with the existing metadata languages (e.g. ecological meta-data language).

4. The encouraging community involvement stems from SPI-Bird's decentralized approach: research groups retain full control over data use and their way of data management, while SPI-Birds creates tailored pipelines to convert each unique data format into a standard format. We outline the lessons learned, so that other communities (e.g. those working on other taxa) can adapt our successful model. Creating community-specific hubs (such as ours, COMADRE for animal demography, etc.) will aid much-needed large-scale ecological data integration.

National statistical systems are facing significant challenges. These challenges arise from increasing demands for high quality and trustworthy data to guide decision making, coupled with the rapidly changing landscape of the data revolution. To help create a mechanism for learning amongst national statistical systems, the World Bank has developed improved Statistical Performance Indicators (SPI) to monitor the statistical performance of countries. The SPI focuses on five key dimensions of a country’s statistical performance: (i) data use, (ii) data services, (iii) data products, (iv) data sources, and (v) data infrastructure. This will replace the Statistical Capacity Index (SCI) that the World Bank has regularly published since 2004.The SPI focus on five key pillars of a country’s statistical performance: (i) data use, (ii) data services, (iii) data products, (iv) data sources, and (v) data infrastructure. The SPI are composed of more than 50 indicators and contain data for 186 countries. This set of countries covers 99 percent of the world population. The data extend from 2016-2023, with some indicators going back to 2004.For more information, consult the academic article published in the journal Scientific Data. https://www.nature.com/articles/s41597-023-01971-0.

For further details, please refer to https://documents.worldbank.org/en/publication/documents-reports/documentdetail/815721616086786412/measuring-the-statistical-performance-of-countries-an-overview-of-updates-to-the-world-bank-statistical-capacity-index

This data has been superseded by a newer version of the dataset. Please refer to NOAA's Climate Divisional Database for more information. The U.S. Climate Divisional Dataset provides data access to current U.S. temperature, precipitation and drought indeces. Divisional indices included are: Precipitation Index, Palmer Drought Severity Index, Palmer Hydrological Drought Index, Modified Palmer Drought Severity Index, Temperature, Palmer Z Index, Cooling Degree Days, Heating Degree Days, 1-Month Standardized Precipitation Index (SPI), 2-Month (SPI), 3-Month (SPI), 6-Month (SPI),12-Month (SPI) and the 24-Month (SPI). All of these Indices, except for the SPI, are available for Regional, State and National views as well. There are 344 climate divisions in the CONUS. For each climate division, monthly station temperature and precipitation values are computed from the daily observations. The divisional values are weighted by area to compute statewide values and the statewide values are weighted by area to compute regional values. The indices were computed using daily station data from 1895 to present.

Values of the Standardized Precipitation Index (SPI) over The Netherlands. The period is 3 months.

The Standardized Precipitation Index (SPI) was generated for certain Environment Canada long-term climate stations in Ontario. The SPI quantifies the precipitation deficit and surplus for multiple time scales , including: * one month * three months * six months * nine months * 12 months * 24 months You can use the SPI to study the impact of dry and wet weather conditions to create comprehensive water management approaches. The SPI data package is distributed as a Microsoft Access Geodatabase. This is a legacy dataset that we no longer maintain or support. The documents referenced in this record may contain URLs (links) that were valid when published, but now link to sites or pages that no longer exist.

Droughts are natural occurring events in which dry conditions persist over time. Droughts are complex to characterize because they depend on water and energy balances at different temporal and spatial scales. The Standardized Precipitation Index (SPI) is used to analyze meteorological droughts. SPI estimates the deviation of precipitation from the long-term probability function at different time scales (e.g. 1, 3, 6, 9, or 12 months). SPI only uses monthly precipitation as an input, which can be helpful for characterizing meteorological droughts. Other variables should be included (e.g. temperature or evapotranspiration) in the characterization of other types of droughts (e.g. agricultural droughts).This layer shows the SPI index at different temporal periods calculated using the SPEI library in R and precipitation data from CHIRPS data set.Sources:Climate Hazards Center InfraRed Precipitation with Station data (CHIRPS)SPEI R library

Chad TD: SPI: Pillar 4 Data Sources Score: Scale 0-100 data was reported at 17.792 NA in 2022. This records a decrease from the previous number of 18.017 NA for 2021. Chad TD: SPI: Pillar 4 Data Sources Score: Scale 0-100 data is updated yearly, averaging 17.792 NA from Dec 2016 (Median) to 2022, with 7 observations. The data reached an all-time high of 20.908 NA in 2016 and a record low of 13.933 NA in 2017. Chad TD: SPI: Pillar 4 Data Sources Score: Scale 0-100 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Chad – Table TD.World Bank.WDI: Governance: Policy and Institutions. The data sources overall score is a composity measure of whether countries have data available from the following sources: Censuses and surveys, administrative data, geospatial data, and private sector/citizen generated data. The data sources (input) pillar is segmented by four types of sources generated by (i) the statistical office (censuses and surveys), and sources accessed from elsewhere such as (ii) administrative data, (iii) geospatial data, and (iv) private sector data and citizen generated data. The appropriate balance between these source types will vary depending on a country’s institutional setting and the maturity of its statistical system. High scores should reflect the extent to which the sources being utilized enable the necessary statistical indicators to be generated. For example, a low score on environment statistics (in the data production pillar) may reflect a lack of use of (and low score for) geospatial data (in the data sources pillar). This type of linkage is inherent in the data cycle approach and can help highlight areas for investment required if country needs are to be met.;Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators);Weighted average;

Standardised Precipitation Index (SPI) data for Integrated Hydrological Units (IHU) groups (Kral et al. [1]). SPI is a drought index based on the probability of precipitation for a given accumulation period as defined by McKee et al. [2]. SPI is calculated for different accumulation periods: 1, 3, 6, 12, 18, 24 months. Each of these is in turn calculated for each of the twelve calendar months. Note that values in monthly (and for longer accumulation periods also annual) time series of the data therefore are likely to be autocorrelated. The standard period which was used to fit the gamma distribution is 1961-2010. The dataset covers the period from 1961 to 2012. [1] Kral, F., Fry, M., Dixon, H. (2015). Integrated Hydrological Units of the United Kingdom: Groups. NERC-Environmental Information Data Centre https://doi.org/10.5285/f1cd5e33-2633-4304-bbc2-b8d34711d902 [2] McKee, T. B., Doesken, N. J., Kleist, J. (1993). The Relationship of Drought Frequency and Duration to Time Scales. Eighth Conference on Applied Climatology, 17-22 January 1993, Anaheim, California.

SpiArcBase is a software developed for the treatment of Sediment Profile images (SPIs). Sediment Profile Images (SPIs) are widely used for benthic ecological quality assessment under various environmental stressors. The processing of the information contained in SPIs is slow and its interpretation is largely operator dependent. SpiArcBase enhances the objectivity of the information extracted from SPIs, especially for the assessment of the apparent Redox Potential Discontinuity (aRPD). This software allows the user to create and manage a database containing original SPIs and corresponding derived pieces of information. Once you have downloaded it, you can ask for help and stablish a helpdesk.