Data tables are in two excel file worksheets. The first sheet, labeled 'Fitted Filtration Efficiency' has columns with subject, mask & condition (baseline or with clip), Chamber Relative Humidity (%) and Temperature (degrees Celsius), the Overall Fitted Filtration Efficiency mean (across four exercises) and standard deviation. The second sheet, labeled 'Sex' has columns with the subject number and their biological sex (F = Female, M = Male). This dataset is associated with the following publication: Pennington, E., J. Griffin, E. McInroe, W. Steinhardt, H. Chen, J. Samet, and S. Prince. Variation in the Fitted Filtration Efficiency of Disposable Face Masks by Sex. Journal of Exposure Science and Environmental Epidemiology. Nature Publishing Group, London, UK, s41370-024-00697-4, (2024).

The food dollar series measures annual expenditures by U.S. consumers on domestically produced food. This data series is composed of three primary series—the marketing bill series, the industry group series, and the primary factor series—that shed light on different aspects of the food supply chain. The three series show three different ways to split up the same food dollar. Nominal DataThe FoodDollarDataNominal.xls file and the NominalData.csv file include statistics reported in current year dollars. In the data rows, each row statistic covers a unique combination of year, unit of measurement, table number, and category number. These are defined as follows:YEAR: 1993 to 2015UNITS: reported in both cents per domestic food dollar and total domestic food dollars ($ millions)Real Data The FoodDollarDataReal.xls file and the FoodDollarDataReal.csv file include statistics reported in constant year 2009 dollars. Since the March 30, 2016 update, 2006 data in cents per domestic real food dollar units have been added to the real food dollar series.In the data rows, each row statistic covers a unique combination of year, unit of measurement, table number, and category number. These are defined as follows:YEAR: 1993 to 2014UNITS: reported in both cents per domestic food dollar and total domestic food dollars ($ millions)

The purpose of the SNF study was to improve our understanding of the relationship between remotely sensed observations and important biophysical parameters in the boreal forest. A key element of the experiment was the development of methodologies to measure forest stand characteristics to determine values of importance to both remote sensing and ecology. Parameters studied were biomass, leaf area index, above ground net primary productivity, bark area index and ground coverage by vegetation. Thirty two quaking aspen and thirty one black spruce sites were studied. Sites were chosen in uniform stands of aspen or spruce. The dominant species in the site constituted over 80 percent, and usually over 95 percent, of the total tree density and basal area. Aspen stands were chosen to represent the full range of age and stem density of essentially pure aspen, of nearly complete canopy closure, and greater than two meters in height. Spruce stands ranged from very sparse stands on bog sites, to dense, closed stands on more productive peatlands. Use of multiple plots within each site allowed estimation of the importance of spatial variation in stand parameters. Within each plot, all woody stems greater than two meters in height were recorded by species and the following dimensions were measured: diameter breast height, height of the tree, height of the first live branch, and depth of crown. For each plot, a two meter diameter subplot was defined at the center of each plot. Within this subplot, the percent of ground coverage by plants under one meter in height was determined by species. These data, averaged for the five plots in each site, are presented in this data set (i.e., SNF Forest Understory Cover Data (Table)) in tabular format, e.g. plant species with a count for that species at each site. The same data are presented in the SNF Forest Understory Cover Data data set but are arranged with a row for each species and site and a percent ground coverage for each combination.

Contains the Excel created to compile and analyze all the values for position registered during the stereo versions analysis. The first sheet contains an overview of the analysis of the 7 songs and the following sheets present the individual table for each song. The last sheet also contains a comparison of mean and standard deviation values between both formats analyzed, stereo and surround sound.

The National Pollutant Release Inventory (NPRI) is Canada's public inventory of pollutant releases (to air, water and land), disposals and transfers for recycling. Each file contains annual total releases for the past ten years by media (air, water or land), broken-down by province, industry or substance. Files are in .CSV format. The results can be further broken down using the pre-defined search available at the bottom of the NPRI Data Search webpage. The results returned by the NPRI search engine may differ from the numbers contained in the downloadable files. The online search engine’s results will display releases, disposals and transfers reported by facilities, but does not distinguish between media type (i.e. air, water, land). It also displays facilities reporting only under Ontario Regulation 127/01 and facilities submitting “did not meet criteria” reports. Please consult the following resources to enhance your analysis: - Guide on using and Interpreting NPRI Data: https://www.canada.ca/en/environment-climate-change/services/national-pollutant-release-inventory/using-interpreting-data.html - Access additional data from the NPRI, including datasets and mapping products: https://www.canada.ca/en/environment-climate-change/services/national-pollutant-release-inventory/tools-resources-data/exploredata.html Supplemental Information More NPRI datasets and mapping products are available here: https://www.canada.ca/en/environment-climate-change/services/national-pollutant-release-inventory/tools-resources-data/access.html Supporting Projects: National Pollutant Release Inventory (NPRI)

The graph shows the changes in the impact factor of ^ and its corresponding percentile for the sake of comparison with the entire literature. Impact Factor is the most common scientometric index, which is defined by the number of citations of papers in two preceding years divided by the number of papers published in those years.

Objectives: Inborn error of immunity (IEI) comprises a broad group of inherited immunological disorders that usually display an overlap in many clinical manifestations challenging their diagnosis. The identification of disease-causing variants comprises the gold-standard approach to ascertain IEI diagnosis. The efforts to increase the availability of clinically relevant genomic data for these disorders constitute an important improvement in the study of rare genetic disorders. This work aims to make available whole-exome sequencing (WES) data of Brazilian patients' suspicion of IEI without a genetic diagnosis. We foresee a broad use of this dataset by the scientific community in order to provide a more accurate diagnosis of IEI disorders.Data description: Twenty singleton unrelated patients treated at four different hospitals in the state of Rio de Janeiro, Brazil were enrolled in our study. Half of the patients were male with mean ages of 9±3, while females were 12±10 years old. The WES was performed in the Illumina NextSeq platform with at least 90% of sequenced bases with a minimum of 30 reads depth. Each sample had an average of 20,274 variants, comprising 116 classified as rare pathogenic or likely pathogenic according to ACMG guidelines. The genotype-phenotype association was impaired by the lack of detailed clinical and laboratory information, besides the unavailability of molecular and functional studies which, comprise the limitations of this study. Overall, the access to clinical exome sequencing data is limited, challenging exploratory analyses and the understanding of genetic mechanisms underlying disorders. Therefore, by making these data available, we aim to increase the number of WES data from Brazilian samples despite contributing to the study of monogenic IEI-disorders.

BackgroundThis study aimed to investigate knowledge, attitude, and practice (KAP) toward coronary heart disease (CHD) secondary prevention among CHD patients.MethodsThis web-based cross-sectional study enrolled patients with CHD who visited the Yangpu District Central Hospital in Shanghai (China) between October 18, 2022, and March 25, 2023. The administered questionnaire assessed demographic information and KAP; factors associated with good practice were identified by multivariate logistic regression.ResultsA total of 507 participants were included in the study, with 361 (71.2%) being male. In terms of education, 125 (24.7%) had a junior high school level or below. The mean scores for knowledge, attitudes, and practices were 31.28 ± 7.30 (possible range: 0–42), 54.09 ± 3.33 (possible range: 12–60), and 35.48 ± 3.36 (possible range: 11–55), respectively. For specific knowledge items on CHD, 57.6% of participants correctly identified that women are more susceptible to CHD. Physical labor and emotional excitement as triggers for CHD were correctly recognized by 94.1%. The need for long-term medication and follow-up after a CHD diagnosis had the highest correctness rate at 98.8%. Additionally, 84.6% correctly understood that recurrence of CHD is possible after PCI surgery. Multivariate analysis indicated that smoking and diabetes status were significantly associated with Practice scores. Current smokers reported lower practice levels than never smokers (OR = 2.858, 95% CI: 1.442–5.662, P = 0.003). Participants with diabetes reported higher practice levels than those without diabetes (OR = 4.169, 95% CI: 2.329–7.463, P < 0.001).ConclusionsPatients with CHD in Shanghai, China, demonstrated good knowledge and positive attitudes toward CHD secondary prevention, although there were some gaps in actual practice behaviors. Enhancing targeted educational interventions and support systems in clinical settings may help bridge these gaps and improve adherence to recommended preventive practices.

The tables are in Excel format and provide data to accompany each topic. The Methods tables provide more detailed analysis of survey response than the summary tables in the Methods report. They also include details of the quality assessments of the blood, saliva and urine samples to accompany Section 9 of the Methods report. Adults are defined as people aged 16 and over.

Data using the definition of alcohol-specific deaths in addition to a count of deaths caused by chronic hepatitis and fibrosis and cirrhosis of the liver in the UK

The tables provide data for adults (defined as people aged 16 and over) and children (defined as people aged between 0 and 15).

This layer shows housing costs as a percentage of household income by age. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Income is based on earnings in past 12 months of survey. This layer is symbolized to show the predominant housing type for householders where the householder is age 65+ and spending at least 30% of their income on housing. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B25072, B25093 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

UPDATED on October 15 2020 After some mistakes in some of the data were found, we updated this data set. The changes to the data are detailed on Zenodo (http://doi.org/10.5281/zenodo.4061807), and an Erratum has been submitted. 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 165 data sources, representing a total of 1668 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).

Dataset extracted from the post 10 Important Questions on Fundamental Analysis of Stocks – Meaning, Parameters, and Step-by-Step Guide on Smart Investello.

The Travel Time to Work indicator compares the mean, or average, commute time for Champaign County residents to the mean commute time for residents of Illinois and the United States as a whole. On its own, mean travel time of all commuters on all mode types could be reflective of a number of different conditions. Congestion, mode choice, changes in residential patterns, changes in the location of major employment centers, and changes in the transit network can all impact travel time in different and often conflicting ways. Since the onset of the COVID-19 pandemic in 2020, the workplace location (office vs. home) is another factor that can impact the mean travel time of an area. We don’t recommend trying to draw any conclusions about conditions in Champaign County, or anywhere else, based on mean travel time alone.

However, when combined with other indicators in the Mobility category (and other categories), mean travel time to work is a valuable measure of transportation behaviors in Champaign County.

Champaign County’s mean travel time to work is lower than the mean travel time to work in Illinois and the United States. Based on this figure, the state of Illinois has the longest commutes of the three analyzed areas.

The year-to-year fluctuations in mean travel time have been statistically significant in the United States since 2014, and in Illinois most recently in 2021 and 2022. Champaign County’s year-to-year fluctuations in mean travel time were statistically significant from 2021 to 2022, the first time since this data first started being tracked in 2005.

Mean travel time data was sourced from the U.S. Census Bureau’s American Community Survey (ACS) 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Travel Time to Work.

Sources: U.S. Census Bureau; American Community Survey, 2024 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (18 November 2025).; U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (16 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (10 October 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (17 October 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (29 March 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (29 March 2021).; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).

These data were compiled for a manuscript in which 1) we develop a water temperature model for the major river segments and tributaries of the Colorado River basin, including the Colorado, Green, Yampa, White, and San Juan rivers; 2) we link modeled water temperature to fish population data to predict the probability native and nonnative species will be common in the future in a warming climate; and 3) assess the degree to which dams create thermal discontinuity in summer in river segments across the western US. Per goal #1, we developed a water temperature model using data spanning 1985-2015 that predicts water temperature every 1 mile (1.6-km) in rivers both now and in the future due to the potential influence of climate change and human decisions on water storage in reservoirs that affect temperature. Data inputs to the water temperature model include air temperature, discharge, water temperature, and solar radiation. Base model data are included in the 'WaterT Model-Base Data' and 'WaterT Model-Base Summary' data tables, future air temperature predictions are included in the 'WaterT Model-CMIP AirT' data table, and reservoir elevations are included in the 'WaterT Model-Reservoir Storage' data table. These data were used to generate water temperature predictions used in Figures 1-4 in the associated manuscript. Per goal #2, we calculated the number of Thermally Suitable Days (TSDs) in each river segment for Colorado pikeminnow (Ptychocheilus lucius), razorback sucker (Xyrauchen texanus), humpback chub (Gila cypha), smallmouth bass (Micropterus dolomieu), red shiner (Cyprinella lutrensis), and channel catfish (Ictalurus punctatus) using species-specific thermal minimums, maximums, and optimums for growth ('TSD-TemperatureTolerance' data table) and mean monthly predicted water temperatures by reach from the base model ('TSD-WaterT' data table). We compare summarized decadal-scale endangered humpback chub population estimates from 1990-2018 for multiple populations relative to TSDs in the 'Humpback Chub PopEst' data table. We include endangered Colorado Pikeminnow population estimates from 2001-2018 relative to smallmouth bass removals in the 'Colorado Pikeminnow PopEst' data table. These are data presented in Figure 3 in the associated manuscript. Circle size is weighted to TSDs per data in the data table. Per goal #3, we take the difference between mean July air temperature and water temperature released from dams and compare this 'discontinuity' to the depth from which water was withdrawn from the reservoir in the 'Therm Discontinuity Data' data table. Data summarized for these 3 goals were obtained from the U.S. Geological Survey, National Water Information System, Glen Canyon Dam Monitoring and Research Center (USGS), U.S. Fish and Wildlife Service San Juan River Basin Recovery Implementation Program, NOAA National Climatic Data Center, National Solar Radiation Database, the USBR Hydromet Data System, and from published literature.

Code Tables Used In Landings System. These tables assign meanings to the codes that appear in the data tables. Code tables exist for species, gear, state, county, dealer, and distance from shore

This data table provides a collection of information from peer-reviewed autism prevalence studies. Information reported from each study includes the autism prevalence estimate and additional study characteristics (e.g., case ascertainment and criteria). A PubMed search was conducted to identify studies published at any time through September 2020 using the search terms: autism (title/abstract) OR autistic (title/abstract) AND prevalence (title/abstract). Data were abstracted and included if the study fulfilled the following criteria: • The study was published in English; • The study produced at least one autism prevalence estimate; and • The study was population-based (any age range) within a defined geographic area.

The ocean waters surrounding New Zealand vary in temperature from north to south. They interact with heat and moisture in the atmosphere and affect our weather. Sea surface temperature changes with climate drivers such as El Niño, and will change with climate change. The sea surface temperature anomaly provides an indication of the heat change in the ocean. Long-term changes and short-term variability in sea-surface temperatures can affect marine processes, habitats, and species. some species may find it hard to survive in changing environmental conditions. The oceanic sea surface temperature data comes from the NIWA Sea surface temperature Archive (NSA). There are 2 datasets, NSA Annual Means and NSA Annual Anomolies ,covering the Tasman, subtropical (STW) and Southern Antarctic (SAW) area and the total area. The data is available from 1993 to 2013 and the unit of measure is degrees Celsius . For more information please see: Uddstrom, MJ (2015) Sea Surface Temperature Data and Analysis for the 2015 Synthesis Report. For Ministry for the Environment. Available at https://data.mfe.govt.nz/x/hRbGUJ on the Ministry for the Environment dataservice (https://data.mfe.govt.nz). Trend results can be found in the excel file "Sea surface temperature trend statistics" found at https://data.mfe.govt.nz/x/DGXFS6. This dataset relates to the "Sea surface temperature" measure on the Environmental Indicators, Te taiao Aotearoa website.