Selected demographic, social, economic, and housing estimates data by community district/PUMA (Public Use Micro Data Sample Area). Three year estimates of population data from the Census Bureau's American Community Survey

Contains demographic profile information from the Australian Bureau of Statistics (ABS) 2016 Census of Population and Housing. Data has been aggregated based on the top 12 countries of birth for residents.

This data has been derived from the ABS Census TableBuilder online data tool (http://www.abs.gov.au/websitedbs/D3310114.nsf/Home/2016%20TableBuilder) by Australian Bureau of Statistics, used under CC 4.0.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Selected demographic and housing estimates data citywide and by borough. Five year estimates of population data from the Census Bureau's American Community Survey.

A random sample of households were invited to participate in this survey. In the dataset, you will find the respondent level data in each row with the questions in each column. The numbers represent a scale option from the survey, such as 1=Excellent, 2=Good, 3=Fair, 4=Poor. The question stem, response option, and scale information for each field can be found in the var "variable labels" and "value labels" sheets. VERY IMPORTANT NOTE: The scientific survey data were weighted, meaning that the demographic profile of respondents was compared to the demographic profile of adults in Bloomington from US Census data. Statistical adjustments were made to bring the respondent profile into balance with the population profile. This means that some records were given more "weight" and some records were given less weight. The weights that were applied are found in the field "wt". If you do not apply these weights, you will not obtain the same results as can be found in the report delivered to the Bloomington. The easiest way to replicate these results is likely to create pivot tables, and use the sum of the "wt" field rather than a count of responses.

Abstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied The data was extracted from the …Show full descriptionAbstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied The data was extracted from the NSW BioNet - Atlas system. Specifically the data table has been extracted from the Atlas of NSW Wildlife / threatened species profile module. This is the primary data that is displayed in the our TSp Web application http://www.environment.nsw.gov.au/threatenedspeciesapp/profile.aspx?id=10616. We intend to complete revise the way we create, manage and maintain K&P distributions over the next 12-18mths. Dataset History This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied: (1) Could you please confirm following data table joining method? [OBJECTID] in shape file "CMA_sub_regions" is joining with [objected] in sheet "ProfileDistribution" in "ProfileDistribution.xls" file. Then, [ProfileID] in sheet "ProfileDistribution" is joining with [ProfileID] in sheet "Profiles" in same "ProfileDistribution.xls" file. For example, for ACT (OBJECTID:73), I can find 39 ProfileIDs in ProfileDistribution using OBJECTID 73. If I want to see details of those 39 species or ECs, go to sheet "Profiles" based on these 39 ProfileIDs. (2) For sheet ProfileDistribution, What is the meaning of Occurrence and its content class such as K, P? 'K' (Known) indicates a confirmed record within any CMA sub-region. This data is stored as a distribution layer in the TS Profiles module of Bionet-Atlas. 'P' (Predicted) indicates that this threatened species or entity is likely to occur in a CMA sub-region . This is an expert opinion, managed in the TS Profiles module. We recommend you disregard all Predicted data. (3) For sheet Profiles . What is the meaning of ProfileType's class such as Other, Population, SpeciesCode? . What is the meaning of SpeciesType's class such as FA, FL, FU? "ProfileType" attributes the type of profiles: SpeciesCode = threatened species; Populations = threatened populations; Other = threatened ecological community, or Threatening process as Other. Simply ameans of systematicly separating off species and population distributions. "SpeciesType" is to indicate whether the threatened species or population profile is Flora (FL), Fauna (FA) or Fungi (FU). (4) For shape file "CMA_sub_regions" We are looking for a unique and meaningful name for the smallest spatial unit. Is [DISPLAY2] such a name? "DISPLAY2" that would be the shortest, unique name. (5) I only found Threatened Ecological Communities under field "GeneralType" in "Profiles". Could you please let me know where I can find the indicator for Threatened Species? Your interpretation of TEC indicator is correct. Threatened Species are indicated in Profiles by ProfileType = SpeciesCode. Dataset Citation NSW Office of Environment and Heritage (2013) Spatial Threatened Species and Communities (TESC) NSW 20131129. Bioregional Assessment Source Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/a6664894-1489-46d1-a6ca-16f9ab519a28.

Community citation profile (Example).

The City of Bloomington contracted with National Research Center, Inc. to conduct the 2019 Bloomington Community Survey. This was the second time a scientific citywide survey had been completed covering resident opinions on service delivery satisfaction by the City of Bloomington and quality of life issues. The first was in 2017. The survey captured the responses of 610 households from a representative sample of 3,000 residents of Bloomington who were randomly selected to complete the survey. VERY IMPORTANT NOTE: The scientific survey data were weighted, meaning that the demographic profile of respondents was compared to the demographic profile of adults in Bloomington from US Census data. Statistical adjustments were made to bring the respondent profile into balance with the population profile. This means that some records were given more "weight" and some records were given less weight. The weights that were applied are found in the field "wt". If you do not apply these weights, you will not obtain the same results as can be found in the report delivered to the City of Bloomington. The easiest way to replicate these results is likely to create pivot tables, and use the sum of the "wt" field rather than a count of responses.

This dataset contains data collected within limestone cedar glades at Stones River National Battlefield (STRI) near Murfreesboro, Tennessee. This dataset contains information on soil microbial metabolic diversity for soil samples obtained from certain quadrat locations (points) within 12 selected cedar glades. This information derives from substrate utilization profiles based on Biolog EcoPlates (Biolog, Inc., Hayward, CA, USA) which were inoculated with soil slurries containing the entire microbial community present in each soil sample. EcoPlates contain 31 sole-carbon substrates (present in triplicate on each plate) and one blank (control) well. Once the microbial community from a soil sample is inoculated onto the plates, the plates are incubated and absorbance readings are taken at intervals.For each quadrat location (point), one soil sample was obtained under sterile conditions, using a trowel wiped with methanol and rinsed with distilled water, and was placed into an autoclaved jar with a tight-fitting lid and placed on ice. Soil samples were transported to lab facilities on ice and immediately refrigerated. Within 24 hours after being removed from the field, soil samples were processed for community level physiological profiling (CLPP) using Biolog EcoPlates. First, for each soil sample three measurements were taken of gravimetric soil water content using a Mettler Toledo HB43 halogen moisture analyzer (Mettler Toledo, Columbus, OH, USA) and the mean of these three SWC measurements was used to calculate the 10-gram dry weight equivalent (DWE) for each soil sample. For each soil sample, a 10-gram DWE of fresh soil was added to 90 milliliters of sterile buffer solution in a 125-milliliter plastic bottle to make the first dilution. Bottles were agitated on a wrist-action shaker for 20 minutes, and a 10-milliliter aliquot was taken from each sample using sterilized pipette tips and added to 90 milliliters of sterile buffer solution to make the second dilution. The bottle containing the second dilution for each sample was agitated for 10 seconds by hand, poured into a sterile tray, and the second dilution was inoculated directly onto Biolog EcoPlates using a sterilized pipette set to deliver 150 microliters into each well. Each plate was immediately covered, placed in a covered box and incubated in the dark at 25 degrees Celcius. Catabolism of each carbon substrate produced a proportional color change response (from the color of the inoculant to dark purple) due to the activity of the redox dye tetrazolium violot (present in all wells including blanks). Plates were read at intervals of 24 hours, 48 hours, 72 hours, 96 hours and 120 hours after inoculation using a Biolog MicroStation plate reader (Biolog, Inc., Hayward, CA, USA) reading absorbance at 590 nanometers.For each soil sample and at each incubation time point, raw absorbance values were transformed according to the equations:T = (C-R) / AWCD; and AWCD = [Σ (C – R)] / nwhere T represents transformed substrate-level response values, C is the absorbance value of control wells (mean of 3 controls), R is the mean absorbance of the response wells (3 wells per carbon substrate), AWCD is average well color development for the plate, and n is the number of carbon substrates (31 for EcoPlates). To integrate time-series data from multiple EcoPlate readings (both for AWCD and also for individual substrates, T), the area under the incubation curve, from 48 hours to 120 hours of incubation time, was calculated.To assess community-level microbial diversity, the Shannon-Weaver index (H) was calculated as follows:H = - ∑ p(ln p)where p is the ratio of the activity of each substrate (T values, area under the incubation curve) to the sum of the activities of all substrates for a given EcoPlate. Thus, the numeric values contained in the fields of this dataset represent H values (Shannon-Weaver index of diversity) based on substrate-utilization diversity of the entire microbial community of each soil sample. Higher values indicate that the entire microbial community metabolized a greater diversity of substrates present on the EcoPlates during the incubation period under consideration. Detailed descriptions of experimental design, field data collection procedures, laboratory procedures, and data analysis are presented in Cartwright (2014).References:Cartwright, J. (2014). Soil ecology of a rock outcrop ecosystem: abiotic stresses, soil respiration, and microbial community profiles in limestone cedar glades. Ph.D. dissertation, Tennessee State UniversityCofer, M., Walck, J., and Hidayati, S. (2008). Species richness and exotic species invasion in Middle Tennessee cedar glades in relation to abiotic and biotic factors. The Journal of the Torrey Botanical Society, 135(4), 540–553.Garland, J., & Mills, A. (1991). Classification and characterization of heterotrophic microbial communities on the basis of patterns of community-level sole-carbon-source utilization. Applied and environmental microbiology, 57(8), 2351–2359.Garland, J. (1997). Analysis and interpretation of community‐level physiological profiles in microbial ecology. FEMS Microbiology Ecology, 24, 289–300.Hackett, C. A., & Griffiths, B. S. (1997). Statistical analysis of the time-course of Biolog substrate utilization. Journal of Microbiological Methods, 30(1), 63–69.Insam, H. (1997). A new set of substrates proposed for community characterization in environmental samples. In H. Insam & A. Rangger (Eds.), Microbial Communities: Functional versus Structural Approaches(pp. 259–260). New York: Springer.Preston-Mafham, J., Boddy, L., & Randerson, P. F. (2002). Analysis of microbial community functional diversity using sole-carbon-source utilisation profiles - a critique. FEMS microbiology ecology, 42(1), 1–14. doi:10.1111/j.1574-6941.2002.tb00990.x

Although soil and agronomy data collection in Ethiopia has begun over 60 years ago, the data are hardly accessible as they are scattered across different organizations, mostly held in the hands of individuals (Ashenafi et al.,2020; Tamene et al.,2022), which makes them vulnerable to permanent loss. Cognizant of the problem, the Coalition of the Willing (CoW) for data sharing and access was created in 2018 with joint support and coordination of the Alliance Bioversity-CIAT and GIZ (https://www.ethioagridata.com/index.html). Mobilizing its members, the CoW has embarked on data rescue operations including data ecosystem mapping, collation, and curation of the legacy data, which was put into the central data repository for its members and the wider data user’s community according to the guideline developed based on the FAIR data principles and approved by the CoW. So far, CoW managed to collate and rescue about 20,000 legacy soil profile data and over 38,000 crop responses to fertilizer data (Tamene et al.,2022). The legacy soil profile dataset (consisting of Profiles Site = 2,612 observations with 37 variables; Profiles Layer Field = 6,150 observations with 64 variables; Profiles Layer Lab= 4,575 observations with 76 variables) is extracted, transformed, and uploaded into a harmonized template (adapted from Batjes 2022; Leenaars et al, 2014) from the below source: Bilateral Ethiopian-Netherlands Effort for Food, Income and Trade (BENEFIT) Partnership which is a portfolio of five programs (ISSD, Cascape, ENTAG, SBN, and REALISE) and is funded by the government of the Kingdom of Netherlands through its embassy in Addis Ababa. The Cascape program has conducted several studies, including soil surveys and mappings in AGP weredas in Tigray, Amhara, Oromia,and SNNPR in Ethiopia. The program (then Cascape project) as a collaborator of MoA/ATA has produced a map-database and soildataset of the major soil types (at 250-m resolution) of the landscapes of the 30 Cascape intervention-AGP weredas studied in 2013-2015: 5 of Tigray, 5 of Amhara, 15 of Oromia, and 5 of SNNPR.

Reference: Although soil and agronomy data collection in Ethiopia has begun over 60 years ago, the data are hardly accessible as they are scattered across different organizations, mostly held in the hands of individuals (Ashenafi et al.,2020; Tamene et al.,2022), which makes them vulnerable to permanent loss. Cognizant of the problem, the Coalition of the Willing (CoW) for data sharing and access was created in 2018 with joint support and coordination of the Alliance Bioversity-CIAT and GIZ (https://www.ethioagridata.com/index.html). Mobilizing its members, the CoW has embarked on data rescue operations including data ecosystem mapping, collation, and curation of the legacy data, which was put into the central data repository for its members and the wider data user’s community according to the guideline developed based on the FAIR data principles and approved by the CoW. So far, CoW managed to collate and rescue about 20,000 legacy soil profile data and over 38,000 crop responses to fertilizer data (Tamene et al.,2022). The legacy soil profile dataset (consisting of Profiles Site = 2,612 observations with 37 variables; Profiles Layer Field = 6,150 observations with 64 variables; Profiles Layer Lab= 4,575 observations with 76 variables) is extracted, transformed, and uploaded into a harmonized template (adapted from Batjes 2022; Leenaars et al, 2014) from the below source: Bilateral Ethiopian-Netherlands Effort for Food, Income and Trade (BENEFIT) Partnership which is a portfolio of five programs (ISSD, Cascape, ENTAG, SBN, and REALISE) and is funded by the government of the Kingdom of Netherlands through its embassy in Addis Ababa. The Cascape program has conducted several studies, including soil surveys and mappings in AGP weredas in Tigray, Amhara, Oromia,and SNNPR in Ethiopia. The program (then Cascape project) as a collaborator of MoA/ATA has produced a map-database and soildataset of the major soil types (at 250-m resolution) of the landscapes of the 30 Cascape intervention-AGP weredas studied in 2013-2015: 5 of Tigray, 5 of Amhara, 15 of Oromia, and 5 of SNNPR.

Reference: Ashenafi, A., Tamene, L., and Erkossa, T. 2020. Identifying, Cataloguing, and Mapping Soil and Agronomic Data in Ethiopia. CIAT Publication No. 506. International Center for Tropical Agriculture (CIAT). Addis Ababa, Ethiopia. 42 p. https://hdl.handle.net/10568/110868 Ashenafi, A., Erkossa, T., Gudeta, K., Abera, W., Mesfin, E., Mekete, T., Haile, M., Haile, W., Abegaz, A., Tafesse, D. and Belay, G., 2022. Reference Soil Groups Map of Ethiopia Based on Legacy Data and Machine Learning Technique: EthioSoilGrids 1.0. EGUsphere, pp.1-40. https://doi.org/10.5194/egusphere-2022-301 Tamene L; Erkossa T; Tafesse T; Abera W; Schultz S. 2021. A coalition of the Willing - Powering data-driven solutions for Ethiopian Agriculture. CIAT Publication No. 518. International Center for Tropical Agriculture (CIAT). Addis Ababa, Ethiopia. 34 p. https://www.ethioagridata.com/Resources/Powering%20Data-Driven%20Solutions%20for%20Ethiopian%20Agriculture.pdf. The Coalition of the Willing (CoW) website: https://www.ethioagridata.com/index.html. Batjes, N.H., 2022. Basic principles for compiling a profile dataset for consideration in WoSIS. CoP report, ISRIC–World Soil Information, Wageningen. Contents Summary, 4(1), p.3. Carvalho Ribeiro, E.D. and Batjes, N.H., 2020. World Soil Information Service (WoSIS)-Towards the standardization and harmonization of world soil data: Procedures Manual 2020. Elias, E.: Soils of the Ethiopian Highlands: Geomorphology and Properties, CASCAPE Project, 648 ALTERRA, Wageningen UR, the Netherlands, library.wur.nl/WebQuery/isric/2259099, 649 2016. Leenaars, J. G. B., van Oostrum, A.J.M., and Ruiperez ,G.M.: Africa Soil Profiles Database, Version 1.2. A compilation of georeferenced and standardised legacy soil profile data for Sub Saharan Africa (with dataset), ISRIC Report 2014/01, Africa Soil Information Service (AfSIS) project and ISRIC – World Soil Information, Wageningen, library.wur.nl/WebQuery/isric/2259472, 2014. Leenaars, J. G. B., Eyasu, E., Wösten, H., Ruiperez González, M., Kempen, B.,Ashenafi, A., and Brouwer, F.: Major soil-landscape resources of the cascape intervention woredas, Ethiopia: Soil information in support to scaling up of evidence-based best practices in agricultural production (with dataset), CASCAPE working paper series No. OT_CP_2016_1, Cascape. https://edepot.wur.nl/428596, 2016. Leenaars, J. G. B., Elias, E., Wösten, J. H. M., Ruiperez-González, M., and Kempen, B.: Mapping the major soil-landscape resources of the Ethiopian Highlands using random forest, Geoderma, 361, https://doi.org/10.1016/j.geoderma.2019.114067, 2020a. 740 . Leenaars, J. G. B., Ruiperez, M., González, M., Kempen, B., and Mantel, S.: Semi-detailed soil resource survey and mapping of REALISE woredas in Ethiopia, Project report to the BENEFIT-REALISE programme, December, ISRIC-World Soil Information, Wageningen, 2020b. TERMS: Access to the data is limited to the CoW members until the national soil and agronomy data-sharing directive of MoA is registered by the Ministry of Justice and released for implementation. DISCLAIMER: The dataset populated in the harmonized template consisting of 76 variables is extracted, transformed, and uploaded from the source document by the CoW. Hence, if any irregularities are observed, the data users have referred to the source document uploaded along with the dataset. Use of the dataset and any consequences arising from using it is the user’s sole responsibility.

Tamene L; Erkossa T; Tafesse T; Abera W; Schultz S. 2021. A coalition of the Willing - Powering data-driven solutions for Ethiopian Agriculture. CIAT Publication No. 518. International Center for Tropical Agriculture (CIAT). Addis Ababa, Ethiopia. 34 p. https://www.ethioagridata.com/Resources/Powering%20Data-Driven%20Solutions%20for%20Ethiopian%20Agriculture.pdf. The Coalition of the Willing (CoW) website: https://www.ethioagridata.com/index.html. Batjes, N.H., 2022. Basic principles for compiling a profile dataset for consideration in WoSIS. CoP report, ISRIC–World Soil Information, Wageningen. Contents Summary, 4(1), p.3. Carvalho Ribeiro, E.D. and Batjes, N.H., 2020. World Soil Information Service (WoSIS)-Towards the standardization and harmonization of world soil data: Procedures Manual 2020. Elias, E.: Soils of the Ethiopian Highlands: Geomorphology and Properties, CASCAPE Project, 648 ALTERRA, Wageningen UR, the Netherlands, library.wur.nl/WebQuery/isric/2259099, 649 2016. Leenaars, J. G. B., van Oostrum, A.J.M., and Ruiperez ,G.M.: Africa Soil Profiles Database, Version 1.2. A compilation of georeferenced and standardised legacy soil profile data for Sub Saharan Africa (with dataset), ISRIC Report 2014/01, Africa Soil Information Service (AfSIS) project and ISRIC – World Soil Information, Wageningen, library.wur.nl/WebQuery/isric/2259472, 2014. Leenaars, J. G. B., Eyasu, E., Wösten, H., Ruiperez González, M., Kempen, B.,Ashenafi, A., and Brouwer, F.: Major soil-landscape resources of the cascape intervention woredas, Ethiopia: Soil information in support to scaling up of evidence-based best practices in agricultural production (with dataset), CASCAPE working paper series No. OT_CP_2016_1, Cascape. https://edepot.wur.nl/428596, 2016. Leenaars, J. G. B., Elias, E., Wösten, J. H. M., Ruiperez-González, M., and Kempen, B.: Mapping the major soil-landscape resources of the Ethiopian Highlands using random forest, Geoderma, 361, https://doi.org/10.1016/j.geoderma.2019.114067, 2020a. 740 . Leenaars, J. G. B., Ruiperez, M., González, M., Kempen, B., and Mantel, S.: Semi-detailed soil resource survey and mapping of REALISE woredas in Ethiopia, Project report to the BENEFIT-REALISE programme, December, ISRIC-World Soil Information, Wageningen,

This document contains LiftWEC’s social acceptability dataset. The dataset consists of interview transcripts sourced from semi-structured discussions conducted by a LiftWEC researcher and a range of stakeholders relevant to the field of marine renewable energy production. Semi-structured interviews were conducted with relevant actors between September and November of 2021. A snowball sampling approach was used for the selection of actors to be interviewed. Beginning with a small population of socio-political actors, this study developed a larger sample by learning from initial participants and identifying others who were relevant to the study. Interviewees were drawn up through a process of mapping, ensuring that a variety of actors holding different roles and located in different European nations engaged with the study and provided insight. Participants were then selected based upon the likelihood of having a detailed understanding of the emerging problems confronting marine renewable energy. Informed by literature, it was decided to categorise actors within three distinct profiles; (i) socio-political actors, (ii) market actors, and (iii) community actors. Three examples of interview transcripts, one from an actor relating to each of the aforementioned profiles, are presented in this dataset.

The interviews were designed from the outset to allow for the analysis of debate regarding the social acceptance of novel and emerging marine renewable energy. Interviewees were prompted to discuss their perceptions of the current challenges and opportunities facing marine renewable energy technologies and their experience of how social acceptance issues are managed, and provide recommendations for the future. To support the free development and uptake of individual opinions from a variety of stakeholders, interviews were conducted on a one-to-one basis. A semi-structured interview guideline – an example is presented in section 3 of this dataset – was developed to gather data regarding the specific research objectives of the study. The interview guidelines helped to ensure comparability across interviews, especially across different countries and contexts. The questions that are part of the guideline are open questions, i.e., interview partners did not have fixed options for answering them. This provided interviewees with the possibility of freely choosing which aspect they wanted to put an emphasis or which aspects they wanted to mention. Furthermore, semi-structured interviews enabled the interviewer to spontaneously rephrase or add questions if the answers provided by the interviewee left too much room for interpretation or were not fully clear. All interviews lasted for a duration of between 40 minutes and one hour.

This study that these interviews spawned from was conducted in line with the guidelines and standards set by the Queen’s University of Belfast’s Code of Conduct and Integrity in Research and its Policy and Principles on the Ethical Approval of Research. Free and informed consent was obtained from all participants prior to the collection of data from online interviews. All interviewees were provided with a project information sheet and a consent form prior to meeting, and participants were fully briefed on what the research involves. It was also explained how anonymity and confidentiality will be achieved. Permission was also sought for the audio of the meetings to be recorded and participants were made aware of their right to withdraw within one month of data gathering without penalty. Consent was also obtained for the data to be used for research purposes and for future publication. Confidentiality, a hugely important consideration in research, was ensured at all times during the course of the research.

In order to test for the differential abundance of taxa that may drive the differences observed between inferred microbial communities derived from the different DNA isolation procedures, we performed DESeq2 analyses. Here we provide an example for such an analysis from human fecal specimen, examined using 16S rRNA gene profiling. This workflow relates to the article: Berith E. Knudsen, Lasse Bergmark, Patrick Munk, Oksana Lukjancenko, Anders Priemé, Frank M. Aarestrup, Sünje J. Pamp (2016) Impact of Sample Type and DNA Isolation Procedure on Genomic Inference of Microbiome Composition. mSystems Oct 2016, 1 (5) e00095-16; DOI: 10.1128/mSystems.00095-16

http://msystems.asm.org/content/1/5/e00095-16

GCCSA based data for Dwelling Internet Connection by Dwelling Structure, in General Community Profile (GCP), 2016 Census. Count of occupied private dwellings. Excludes 'Visitors only' and 'Other non-classifiable' households. Records whether any member of the household accesses the internet from the dwelling. This includes accessing the internet through a desktop/laptop computer, mobile or smart phone, tablet, music or video player, gaming console, smart TV or any other devices. It also includes accessing through any type of connection for example ADSL, fibre, cable, wireless, satellite and mobile broadband (3G/4G). The data is by GCCSA 2016 boundaries. Periodicity: 5-Yearly. Note: There are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals. For more information visit the data source: http://www.abs.gov.au/census.

SA4 based data for Dwelling Internet Connection by Dwelling Structure, in General Community Profile (GCP), 2016 Census. Count of occupied private dwellings. Excludes 'Visitors only' and 'Other non-classifiable' households. Records whether any member of the household accesses the internet from the dwelling. This includes accessing the internet through a desktop/laptop computer, mobile or smart phone, tablet, music or video player, gaming console, smart TV or any other devices. It also includes accessing through any type of connection for example ADSL, fibre, cable, wireless, satellite and mobile broadband (3G/4G). The data is by SA4 2016 boundaries. Periodicity: 5-Yearly. Note: There are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals. For more information visit the data source: http://www.abs.gov.au/census.

IOOS Sensor Observation Service (SOS) Server for NANOOS, the Northwest Association of Networked Ocean Observing Systems (http://nanoos.org). Provides access to marine in-situ observation data for the US Pacific Northwest and lower British Columbia, from the NANOOS asset data store harvested and integrated by NVS (NANOOS Visualization System, http://nvs.nanoos.org). To avoid data duplication, currently only assets not otherwise available to the IOOS Catalog (http://catalog.ioos.us) are accessible through this SOS server; for example, assets from most federal agencies are not accessible on this server, but they are available on the NVS application. This NANOOS service is run by the 52North IOOS SOS server software, and complies with the IOOS SOS "Milestone 1" service profile (https://code.google.com/p/ioostech/wiki/SOSGuidelines).

This station provides the following variables: Air temperature, Dew point temperature, Fractional saturation of oxygen in sea water, Mass concentration of chlorophyll in sea water, Mass concentration of oxygen in sea water, Net downward shortwave flux in air, Relative humidity, Sea water salinity, Sea water sigma t, Sea water temperature, Surface air pressure, Surface downwelling photosynthetic radiative flux in air, Wind from direction, Wind speed, Wind speed of gust

SA1 based data for Dwelling Internet Connection by Dwelling Structure, in General Community Profile (GCP), 2016 Census. Count of occupied private dwellings. Excludes 'Visitors only' and 'Other non-classifiable' households. Records whether any member of the household accesses the internet from the dwelling. This includes accessing the internet through a desktop/laptop computer, mobile or smart phone, tablet, music or video player, gaming console, smart TV or any other devices. It also includes accessing through any type of connection for example ADSL, fibre, cable, wireless, satellite and mobile broadband (3G/4G). The data is by SA1 2016 boundaries. Periodicity: 5-Yearly. Note: There are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals. For more information visit the data source: http://www.abs.gov.au/census.

SA3 based data for Dwelling Internet Connection by Dwelling Structure, in General Community Profile (GCP), 2016 Census. Count of occupied private dwellings. Excludes 'Visitors only' and 'Other non-classifiable' households. Records whether any member of the household accesses the internet from the dwelling. This includes accessing the internet through a desktop/laptop computer, mobile or smart phone, tablet, music or video player, gaming console, smart TV or any other devices. It also includes accessing through any type of connection for example ADSL, fibre, cable, wireless, satellite and mobile broadband (3G/4G). The data is by SA3 2016 boundaries. Periodicity: 5-Yearly. Note: There are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals. For more information visit the data source: http://www.abs.gov.au/census.