The USDA Agricultural Research Service (ARS) recently established SCINet , which consists of a shared high performance computing resource, Ceres, and the dedicated high-speed Internet2 network used to access Ceres. Current and potential SCINet users are using and generating very large datasets so SCINet needs to be provisioned with adequate data storage for their active computing. It is not designed to hold data beyond active research phases. At the same time, the National Agricultural Library has been developing the Ag Data Commons, a research data catalog and repository designed for public data release and professional data curation. Ag Data Commons needs to anticipate the size and nature of data it will be tasked with handling. The ARS Web-enabled Databases Working Group, organized under the SCINet initiative, conducted a study to establish baseline data storage needs and practices, and to make projections that could inform future infrastructure design, purchases, and policies. The SCINet Web-enabled Databases Working Group helped develop the survey which is the basis for an internal report. While the report was for internal use, the survey and resulting data may be generally useful and are being released publicly. From October 24 to November 8, 2016 we administered a 17-question survey (Appendix A) by emailing a Survey Monkey link to all ARS Research Leaders, intending to cover data storage needs of all 1,675 SY (Category 1 and Category 4) scientists. We designed the survey to accommodate either individual researcher responses or group responses. Research Leaders could decide, based on their unit's practices or their management preferences, whether to delegate response to a data management expert in their unit, to all members of their unit, or to themselves collate responses from their unit before reporting in the survey. Larger storage ranges cover vastly different amounts of data so the implications here could be significant depending on whether the true amount is at the lower or higher end of the range. Therefore, we requested more detail from "Big Data users," those 47 respondents who indicated they had more than 10 to 100 TB or over 100 TB total current data (Q5). All other respondents are called "Small Data users." Because not all of these follow-up requests were successful, we used actual follow-up responses to estimate likely responses for those who did not respond. We defined active data as data that would be used within the next six months. All other data would be considered inactive, or archival. To calculate per person storage needs we used the high end of the reported range divided by 1 for an individual response, or by G, the number of individuals in a group response. For Big Data users we used the actual reported values or estimated likely values. Resources in this dataset:Resource Title: Appendix A: ARS data storage survey questions. File Name: Appendix A.pdfResource Description: The full list of questions asked with the possible responses. The survey was not administered using this PDF but the PDF was generated directly from the administered survey using the Print option under Design Survey. Asterisked questions were required. A list of Research Units and their associated codes was provided in a drop down not shown here. Resource Software Recommended: Adobe Acrobat,url: https://get.adobe.com/reader/ Resource Title: CSV of Responses from ARS Researcher Data Storage Survey. File Name: Machine-readable survey response data.csvResource Description: CSV file includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed. This information is that same data as in the Excel spreadsheet (also provided).Resource Title: Responses from ARS Researcher Data Storage Survey. File Name: Data Storage Survey Data for public release.xlsxResource Description: MS Excel worksheet that Includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel

The annual Retail store data CD-ROM is an easy-to-use tool for quickly discovering retail trade patterns and trends. The current product presents results from the 1999 and 2000 Annual Retail Store and Annual Retail Chain surveys. This product contains numerous cross-classified data tables using the North American Industry Classification System (NAICS). The data tables provide access to a wide range of financial variables, such as revenues, expenses, inventory, sales per square footage (chain stores only) and the number of stores. Most data tables contain detailed information on industry (as low as 5-digit NAICS codes), geography (Canada, provinces and territories) and store type (chains, independents, franchises). The electronic product also contains survey metadata, questionnaires, information on industry codes and definitions, and the list of retail chain store respondents.

The Minimum Data Set (MDS) Frequency data summarizes health status indicators for active residents currently in nursing homes. The MDS is part of the Federally-mandated process for clinical assessment of all residents in Medicare and Medicaid certified nursing homes. This process provides a comprehensive assessment of each resident's functional capabilities and helps nursing home staff identify health problems. Care Area Assessments (CAAs) are part of this process, and provide the foundation upon which a resident's individual care plan is formulated. MDS assessments are completed for all residents in certified nursing homes, regardless of source of payment for the individual resident. MDS assessments are required for residents on admission to the nursing facility, periodically, and on discharge. All assessments are completed within specific guidelines and time frames. In most cases, participants in the assessment process are licensed health care professionals employed by the nursing home. MDS information is transmitted electronically by nursing homes to the national MDS database at CMS. When reviewing the MDS 3.0 Frequency files, some common software programs e.g., ‘Microsoft Excel’ might inaccurately strip leading zeros from designated code values (i.e., "01" becomes "1") or misinterpret code ranges as dates (i.e., O0600 ranges such as 02-04 are misread as 04-Feb). As each piece of software is unique, if you encounter an issue when reading the CSV file of Frequency data, please open the file in a plain text editor such as ‘Notepad’ or ‘TextPad’ to review the underlying data, before reaching out to CMS for assistance.

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Excel age range creator for GLA Projections data

This Excel based tool enables users to query the raw single year of age data so that any age range can easily be calculated without having to carry out often complex, and time consuming formulas that could also be open to human error. Each year the GLA demography team produce sets of population projections. On this page each of these datasets since 2009 can be accessed, though please remember that the older versions have been superceded. From 2012, data includes population estimates and projections between 2001 and 2041 for each borough plus Central London (Camden, City of London, Kensington & Chelsea, and Westminster), Rest of Inner Boroughs, Inner London, Outer London and Greater London.

The full raw data by single year of age (SYA) and gender are available as Datastore packages at the links below.

How to use the tool: Simply select the lower and upper age range for both males and females (starting in cell C3) and the spreadsheet will return the total population for the range.

Tip: You can copy and paste the boroughs you are interested in to another worksheet by clicking: Edit then Go To (or Control + G), then Special, and Visible cells only. Then simply copy and 'paste values' of the cells to a new location.

Warning: The ethnic group and ward files are large (around 35MB), and may take some time to download depending on your bandwidth.

Find out more about GLA population projections on the GLA Demographic Projections page

BOROUGH PROJECTIONS

GLA 2009 Round London Plan Population Projections (January 2010) (SUPERSEDED)

GLA 2009 Round (revised) London Plan Population Projections (August 2010) (SUPERCEDED)

GLA 2009 Round (revised) SHLAA Population Projections (August 2010) (SUPERCEDED)

GLA 2010 Round SHLAA Population Projections (February 2011) (SUPERCEDED)

GLA 2011 Round SHLAA Population Projections, High Fertility (December 2011) (SUPERCEDED)

GLA 2011 Round SHLAA Population Projections, Standard Fertility (January 2012) (SUPERCEDED)

GLA 2012 Round SHLAA Population Projections, (December 2012)(SUPERCEDED)

GLA 2012 Round Trend Based Population Projections, (December 2012) (SUPERCEDED)

GLA 2012 Round SHLAA Borough Projections incorporating DCLG 2011 household formation rates, (June 2013) (SUPERCEDED)

GLA 2013 Round Trend Based Population Projections - High (December 2013)

GLA 2013 Round Trend Based Population Projections - Central (December 2013)

GLA 2013 Round Trend Based Population Projections - Low (December 2013)

GLA 2013 Round SHLAA Based Population Projections (February 2014) Spreadsheet now includes national comparator data from ONS.

GLA 2013 Round SHLAA Based Capped Population Projections (March 2014) Spreadsheet includes national comparator data from ONS. This is the recommended file to use.

WARD PROJECTIONS

GLA 2008 round (High) Ward Projections (March 2009) (SUPERSEDED)

GLA 2009 round (revised) London Plan Ward Projections (August 2010) (SUPERCEDED)

GLA 2010 round SHLAA Ward Projections (February 2011) (SUPERCEDED)

GLA 2011 round SHLAA Standard Ward Projections (January 2012) (SUPERCEDED)

GLA 2011 round SHLAA High Ward Projections (January 2012) (SUPERCEDED)

GLA 2012 round SHLAA based Ward Projections (March 2013) (XLS) (SUPERCEDED)

GLA 2012 round SHLAA Ward Projections (March 2013) (XLS) (SUPERCEDED)

GLA 2013 round SHLAA Ward Projections (March 2014)

GLA 2013 round SHLAA Capped Ward Projections (March 2014) This is the recommended file to use.

ETHNIC GROUP PROJECTIONS FOR LOCAL AUTHORITIES

GLA 2012 Round SHLAA Ethnic Group Borough Projections - Interim (May 2013) (SUPERCEDED)

GLA 2012 Round Trend Based Ethnic Group Borough Projections - Interim (May 2013) (SUPERCEDED)

GLA 2012 Round SHLAA Based Ethnic Group Borough Projections - Final (Nov 2013) (SUPERCEDED)

GLA 2012 Round Trend Based Ethnic Group Borough Projections - Final (Nov 2013) (SUPERCEDED)

GLA 2013 Round SHLAA Capped Ethnic Group Borough Projections (August 2014)

This dataset contains air temperature data taken from 54 stations near Barrow, Alaska to monitor the urban heat island (UHI) effect. Data is contained within two Excel files, one of daily average air temperatures and the other of daily air temperature ranges. Both the averages and ranges were calculated from 24 hourly readings.

Violations issued by the Department of Buildings in the City of Chicago from 2006 to the present. The dataset contains more than 1 million records/rows of data and cannot be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Wordpad, to view and search. Violations are always associated to an inspection and there can be multiple violation records to one(1) inspection record. Data fields requiring description are detailed below. VIOLATION DATE: The date the violation was cited. INSPECTION CATEGORY: Inspections are categorized by one of the following: COMPLAINT – Inspection is a result of a 311 Complaint PERIODIC – Inspection is a result of recurring inspection (typically on an annual cycle) PERMIT – Inspection is a result of a Permit REGISTRATION – Inspection is a result of a Registration (typically Vacant Building Registration) PROPERTY GROUP: Properties (lots) in the City of Chicago can typically have multiple point addresses, range addresses and buildings. Examples are corner lots, large lots, lots with front and rear buildings, etc.. As a result, inspections (and their associated violations), permits and complaints related to a single property could have different addresses. This problem can be reconciled by using Property Group. All point and range addresses for a property are assigned the same Property Group key. Data Owner: Buildings Time Period: January 1, 2006 to present Frequency: Data is updated daily Related Applications: Building Data Warehouse http://www.cityofchicago.org/city/en/depts/bldgs/provdrs/inspect/svcs/building_violationsonline.html

Violations issued by the Department of Buildings in the City of Chicago from 2006 to the present. The dataset contains more than 1 million records/rows of data and cannot be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Wordpad, to view and search. Violations are always associated to an inspection and there can be multiple violation records to one(1) inspection record. Data fields requiring description are detailed below. VIOLATION DATE: The date the violation was cited. INSPECTION CATEGORY: Inspections are categorized by one of the following: COMPLAINT – Inspection is a result of a 311 Complaint PERIODIC – Inspection is a result of recurring inspection (typically on an annual cycle) PERMIT – Inspection is a result of a Permit REGISTRATION – Inspection is a result of a Registration (typically Vacant Building Registration) PROPERTY GROUP: Properties (lots) in the City of Chicago can typically have multiple point addresses, range addresses and buildings. Examples are corner lots, large lots, lots with front and rear buildings, etc.. As a result, inspections (and their associated violations), permits and complaints related to a single property could have different addresses. This problem can be reconciled by using Property Group. All point and range addresses for a property are assigned the same Property Group key. Data Owner: Buildings Time Period: January 1, 2006 to present Frequency: Data is updated daily Related Applications: Building Data Warehouse http://www.cityofchicago.org/city/en/depts/bldgs/provdrs/inspect/svcs/building_violationsonline.html

These are results of a series of laboratory experiments to determine if topical application of methoprene and 20-ecdysone can terminate reproductive diapause of the weevil, Ceratapion basicorne, which is a recently permitted biological control agent of yellow starthistle (Centaurea solstitialis). Adult weevils feed on leaves, creating pin holes, and lay eggs inside leaves. Diapausing weevils were treated with various doses of methoprene (0, 0.01, 0.1, 1.0 micrograms) dissolved in acetone in experiments 1 and 2. They were treated sequentially first with acetone or 20-ecdysone (1.0 microgram) and then with methoprene (1.0 microgram) in experiment 3 and were treated with 20-ecdysone followed by methoprene in experiment 4.

• Resource Title: data dictionary.

  File Name: JH Data Dictionary.csv

  Resource Description: description of data fields

  Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/microsoft-365/excel




• Resource Title: experiment 1.

  File Name: JH expt1 data.csv

  Resource Description: Methoprene dissolved in acetone was applied topically at doses of 0.0, 0.01 and 0.1 and 1.0 μg per female weevil, and the number of feeding holes and eggs were recorded daily on cut leaves of yellow starthistle at room temperature (12 h photoperiod, temperature range 17 to 21°C).

  Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/microsoft-365/excel




• Resource Title: experiment 2.

  File Name: JH expt2 data.csv

  Resource Description: Methoprene dissolved in acetone was applied topically at doses of 0.0 and 1.0 μg to female weevils that did not produce eggs in experiment 1. The number of feeding holes and eggs were recorded daily on cut leaves of yellow starthistle at room temperature (12 h photoperiod, temperature range 17 to 21°C).

  Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/microsoft-365/excel




• Resource Title: experiment 3.

  File Name: JH expt3 data.csv

  Resource Description: Three types of treatments were applied with sequential applications 2 days apart: 1) acetone + acetone [AA: control], 2) acetone + methoprene [AM], and 20-ecdysone + methoprene 174 [2M]. All doses were 1.0 μg. The number of feeding holes and eggs were recorded every 2 days on cut leaves of yellow starthistle at room temperature (12 h photoperiod, temperature range 17 to 21°C).

  Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/microsoft-365/excel




• Resource Title: experiment 4.

  File Name: JH expt4 data.csv

  Resource Description: Females from experiment 3 that did not oviposit consistently were treated with 1.0 μg of 20-ecdysone followed 2 days later by 1.0 μg of methoprene. The treatments AA, AM, 2M refer to experiment 3. The number of feeding holes and eggs were recorded every 2 days on cut leaves of yellow starthistle at room temperature (12 h photoperiod, temperature range 17 to 21°C).

  Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/microsoft-365/excel

Have you ever wanted to create a quick thematic map of London but lacked the GIS skills or software to do it yourself?

These free mapping tools from the GLA Intelligence Unit allows the user to input their own data to create an instant map that can be copied over into Word or another application of your choice. The user can also copy over the legend, and add labels.

The template allows the user to select either 4 or 5 ranges, and it even has a function to change the colours on the map (default colours are blue).

The tool now also allows users to input their own ranges, which will override the automatic ranges.

There is: Standard borough thematic map

Borough thematic map for categories (as opposed to numbers).

And ward maps for individual boroughs see list below.

Copyright notice: If you publish these maps, a copyright notice must be included within the report saying: "Contains Ordnance Survey data © Crown copyright and database rights."

Ward maps

Ward mapping tools for each borough have also been created. Select the borough you require from the list below:

All London Wards map with pre-2014 boundaries

Barking and Dagenham, Barnet, Bexley, Brent, Bromley, Camden, Croydon, Ealing, Enfield, Greenwich, Hackney (pre 2014), Hammersmith and Fulham, Haringey, Harrow, Havering, Hillingdon, Hounslow, Islington, Kensington and Chelsea (pre 2014), Kingston upon Thames, Lambeth, Lewisham, Merton, Newham, Redbridge, Richmond upon Thames, Southwark, Sutton, Tower Hamlets (pre 2014), Waltham Forest, Wandsworth, Westminster

New ward boundaries - came into effect from May 2014

All London wards map 2014 including the new ward boundaries for Hackney, Kensington and Chelsea, and Tower Hamlets following changes in May 2014.

Hackney, Kensington and Chelsea, Tower Hamlets

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NOTE: Excel 2003 users must 'ungroup' the map for it to work.

Full instructions are contained within the spreadsheet. If you have any questions about these tools please contact Gareth Piggott.

Macros

The tool works in any version of Excel. But the user MUST ENABLE MACROS, for the features to work. There a some restrictions on functionality in the ward maps in Excel 2003 and earlier - full instructions are included in the spreadsheet.

To check whether the macros are enabled in Excel 2003 click Tools, Macro, Security and change the setting to Medium. Then you have to re-start Excel for the changes to take effect. When Excel starts up a prompt will ask if you want to enable macros - click yes.

In Excel 2007 and later, it should be set by default to the correct setting, but if it has been changed, click on the Windows Office button in the top corner, then Excel options (at the bottom), Trust Centre, Trust Centre Settings, and make sure it is set to 'Disable all macros with notification'. Then when you open the spreadsheet, a prompt labelled 'Options' will appear at the top for you to enable macros.

To create your own thematic borough maps in Excel using the ward map tool as a starting point, read these instructions. You will need to be a confident Excel user, and have access to your boundaries as a picture file from elsewhere. The mapping tools created here are all fully open access with no passwords.

What is IPGOD? The Intellectual Property Government Open Data (IPGOD) includes over 100 years of registry data on all intellectual property (IP) rights administered by IP Australia. It also has …Show full descriptionWhat is IPGOD? The Intellectual Property Government Open Data (IPGOD) includes over 100 years of registry data on all intellectual property (IP) rights administered by IP Australia. It also has derived information about the applicants who filed these IP rights, to allow for research and analysis at the regional, business and individual level. This is the 2019 release of IPGOD. How do I use IPGOD? IPGOD is large, with millions of data points across up to 40 tables, making them too large to open with Microsoft Excel. Furthermore, analysis often requires information from separate tables which would need specialised software for merging. We recommend that advanced users interact with the IPGOD data using the right tools with enough memory and compute power. This includes a wide range of programming and statistical software such as Tableau, Power BI, Stata, SAS, R, Python, and Scalar. IP Data Platform IP Australia is also providing free trials to a cloud-based analytics platform with the capabilities to enable working with large intellectual property datasets, such as the IPGOD, through the web browser, without any installation of software. IP Data Platform References The following pages can help you gain the understanding of the intellectual property administration and processes in Australia to help your analysis on the dataset. Patents Trade Marks Designs Plant Breeder’s Rights Updates Tables and columns Due to the changes in our systems, some tables have been affected. We have added IPGOD 225 and IPGOD 325 to the dataset! The IPGOD 206 table is not available this year. Many tables have been re-built, and as a result may have different columns or different possible values. Please check the data dictionary for each table before use. Data quality improvements Data quality has been improved across all tables. Null values are simply empty rather than '31/12/9999'. All date columns are now in ISO format 'yyyy-mm-dd'. All indicator columns have been converted to Boolean data type (True/False) rather than Yes/No, Y/N, or 1/0. All tables are encoded in UTF-8. All tables use the backslash \ as the escape character. The applicant name cleaning and matching algorithms have been updated. We believe that this year's method improves the accuracy of the matches. Please note that the "ipa_id" generated in IPGOD 2019 will not match with those in previous releases of IPGOD.

This Excel file contains all the data collected and generated in this study. It contains 5 tabs; 166 Region Data, 581 Region Data, 166 Region Model, 166 Region Model Accuracy, and 581 Region Model. The two Data tabs contain the collected data for each county in the respective regions. Specifically, county name, FIPS, number of garden centers, number of primary interstate highways (and their numbers) and population. The Model tabs contain the simulation outputs used to obtain the results presented in the manuscript with interactive maps. Readers can use these to run their own new simulations in Matlab (with or without changes to the code), export the results to the corresponding tab in this Excel file and see the results in map form. The 166 Region Model and the 166 Region Model Accuracy tabs also contains the calculations for the quantitative results, i.e. the number of model-data matches, false positives and false negatives, and model accuracy at the county level over time. (XLSX)

Intraspecific competition is a key factor shaping space-use strategies and movement decisions inmany species, yet how and when neighbors utilize shared areas while exhibiting active avoidance of one another is largely un- known. Here, we investigated temporal landscape partitioning in a population of wild baboons (Papio cynocephalus). We used global positioning system (GPS) collars to synchronously record the hourly locations of five baboon social groups for ∼900 days, and we used behavioral, demographic, and life history data to measure factors affecting use of overlap areas. Annual home ranges of neighboring groups overlapped substantially, as predicted (baboons are considered non-territorial), but home ranges overlapped less when space use was assessed over shorter time scales. Moreover, neighboring groups were in close spatial proximity to one another on fewer days than predicted by a null model, suggesting an avoidance-based spacing pattern. At all time scales examined (monthly, bi...

Violations issued by the Department of Buildings in the City of Chicago from 2006 to the present. The dataset contains more than 1 million records/rows of data and cannot be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Wordpad, to view and search. Violations are always associated to an inspection and there can be multiple violation records to one(1) inspection record. Data fields requiring description are detailed below. VIOLATION DATE: The date the violation was cited. INSPECTION CATEGORY: Inspections are categorized by one of the following: COMPLAINT – Inspection is a result of a 311 Complaint PERIODIC – Inspection is a result of recurring inspection (typically on an annual cycle) PERMIT – Inspection is a result of a Permit REGISTRATION – Inspection is a result of a Registration (typically Vacant Building Registration) PROPERTY GROUP: Properties (lots) in the City of Chicago can typically have multiple point addresses, range addresses and buildings. Examples are corner lots, large lots, lots with front and rear buildings, etc.. As a result, inspections (and their associated violations), permits and complaints related to a single property could have different addresses. This problem can be reconciled by using Property Group. All point and range addresses for a property are assigned the same Property Group key. Data Owner: Buildings Time Period: January 1, 2006 to present Frequency: Data is updated daily Related Applications: Building Data Warehouse http://www.cityofchicago.org/city/en/depts/bldgs/provdrs/inspect/svcs/building_violationsonline.html

Violations issued by the Department of Buildings in the City of Chicago from 2006 to the present. The dataset contains more than 1 million records/rows of data and cannot be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Wordpad, to view and search. Violations are always associated to an inspection and there can be multiple violation records to one(1) inspection record. Data fields requiring description are detailed below. VIOLATION DATE: The date the violation was cited. INSPECTION CATEGORY: Inspections are categorized by one of the following: COMPLAINT – Inspection is a result of a 311 Complaint PERIODIC – Inspection is a result of recurring inspection (typically on an annual cycle) PERMIT – Inspection is a result of a Permit REGISTRATION – Inspection is a result of a Registration (typically Vacant Building Registration) PROPERTY GROUP: Properties (lots) in the City of Chicago can typically have multiple point addresses, range addresses and buildings. Examples are corner lots, large lots, lots with front and rear buildings, etc.. As a result, inspections (and their associated violations), permits and complaints related to a single property could have different addresses. This problem can be reconciled by using Property Group. All point and range addresses for a property are assigned the same Property Group key. Data Owner: Buildings Time Period: January 1, 2006 to present Frequency: Data is updated daily Related Applications: Building Data Warehouse http://www.cityofchicago.org/city/en/depts/bldgs/provdrs/inspect/svcs/building_violationsonline.html

On an annual basis (individual hospital fiscal year), individual hospitals and hospital systems report detailed facility-level data on services capacity, inpatient/outpatient utilization, patients, revenues and expenses by type and payer, balance sheet and income statement.

Due to the large size of the complete dataset, a selected set of data representing a wide range of commonly used data items, has been created that can be easily managed and downloaded. The selected data file includes general hospital information, utilization data by payer, revenue data by payer, expense data by natural expense category, financial ratios, and labor information.

There are two groups of data contained in this dataset: 1) Selected Data - Calendar Year: To make it easier to compare hospitals by year, hospital reports with report periods ending within a given calendar year are grouped together. The Pivot Tables for a specific calendar year are also found here. 2) Selected Data - Fiscal Year: Hospital reports with report periods ending within a given fiscal year (July-June) are grouped together.

This dataset contains MS Excel spreadsheet code used to analyze an integrative model that illustrates the inherent trade-offs that will arise among the competing values for landscape space in a boreal forest ecosystem involving interactions among the main trophic compartments of an intact boreal ecosystem, aka “nature”. The model accounts for carbon accumulation via biomass growth of forest trees (timber), carbon loss due to controls from moose herbivory that varies with moose population density (hunting), and soil carbon inputs and release, which together determine net ecosystem productivity (NEP), a measure of carbon sink strength of the ecosystem. We examine how controls on carbon dynamics are altered by forest management for timber harvest, and by moose hunting. We link the ecological dynamics with an economic analysis by assigning a price to carbon stored within the intact boreal forest ecosystem. We then weigh these carbon impacts against the economic benefits of timber production and hunting across a range of moose population densities. Combined, this carbon-bioeconomic program calculates the total ecosystem benefit of a modelled boreal forest system, providing a framework for examining how different forest harvest and moose densities influence the achievement of carbon storage targets, under different levels of carbon pricing. Methods The Excel spreadsheet converts the analytical model into code to numerically calculate carbon benefits. Data in the article figures are generated using the spread code.

"The Statistical Abstract of the United States, published since 1878, is the standard summary of statistics on the social, political, and economic organization of the United States. It is designed to serve as a convenient volume for statistical reference and as a guide to other statistical publications and sources. The latter function is served by the introductory text to each section, the source note appearing below each table, and Appendix I, which comprises the Guide to Sources of Statisti cs, the Guide to State Statistical Abstracts, and the Guide to Foreign Statistical Abstracts. The Statistical Abstract sections and tables are compiled into one Adobe PDF named StatAbstract2009.pdf. This PDF is bookmarked by section and by table and can be searched using the Acrobat Search feature. The Statistical Abstract on CD-ROM is best viewed using Adobe Acrobat 5, or any subsequent version of Acrobat or Acrobat Reader. The Statistical Abstract tables and the metropolitan areas tables from Appendix II are available as Excel(.xls or .xlw) spreadsheets. In most cases, these spreadsheet files offer the user direct access to more data than are shown either in the publication or Adobe Acrobat. These files usually contain more years of data, more geographic areas, and/or more categories of subjects than those shown in the Acrobat version. The extensive selection of statistics is provided for the United States, with selected data for regions, divisions, states, metropolitan areas, cities, and foreign countries from reports and records of government and private agencies. Software on the disc can be used to perform full-text searches, view official statistics, open tables as Lotus worksheets or Excel workbooks, and link directly to source agencies and organizations for supporting information. Except as indicated, figures are for the United States as presently constituted. Although emphasis in the Statistical Abstract is primarily given to national data, many tables present data for regions and individual states and a smaller number for metropolitan areas and cities.Statistics for the Commonwealth of Puerto Rico and for island areas of the United States are included in many state tables and are supplemented by information in Section 29. Additional information for states, cities, counties, metropolitan areas, and other small units, as well as more historical data are available in various supplements to the Abstract. Statistics in this edition are generally for the most recent year or period available by summer 2006. Each year over 1,400 tables and charts are reviewed and evaluated; new tables and charts of current interest are added, continuing series are updated, and less timely data are condensed or eliminated. Text notes and appendices are revised as appropriate. This year we have introduced 72 new tables covering a wide range of subject areas. These cover a variety of topics including: learning disability for children, people impacted by the hurricanes in the Gulf Coast area, employees with alternative work arrangements, adult computer and Internet users by selected characteristics, North America cruise industry, women- and minority-owned businesses, and the percentage of the adult population considered to be obese. Some of the annually surveyed topics are population; vital statistics; health and nutrition; education; law enforcement, courts and prison; geography and environment; elections; state and local government; federal government finances and employment; national defense and veterans affairs; social insurance and human services; labor force, employment, and earnings; income, expenditures, and wealth; prices; business enterprise; science and technology; agriculture; natural resources; energy; construction and housing; manufactures; domestic trade and services; transportation; information and communication; banking, finance, and insurance; arts, entertainment, and recreation; accommodation, food services, and other services; foreign commerce and aid; outlying areas; and comparative international statistics." Note to Users: This CD is part of a collection located in the Data Archive of the Odum Institute for Research in Social Science, at the University of North Carolina at Chapel Hill. The collection is located in Room 10, Manning Hall. Users may check the CDs out subscribing to the honor system. Items can be checked out for a period of two weeks. Loan forms are located adjacent to the collection.

This MSOA atlas provides a summary of demographic and related data for each Middle Super Output Area in Greater London. The average population of an MSOA in London in 2010 was 8,346, compared with 1,722 for an LSOA and 13,078 for a ward.

The profiles are designed to provide an overview of the population in these small areas by combining a range of data on the population, births, deaths, health, housing, crime, commercial property/floorspace, income, poverty, benefits, land use, environment, deprivation, schools, and employment.

If you need to find an MSOA and you know the postcode of the area, the ONS NESS search page has a tool for this.

The MSOA Atlas is available as an XLS as well as being presented using InstantAtlas mapping software. This is a useful tool for displaying a large amount of data for numerous geographies, in one place (requires HTML 5).

CURRENT MSOA BOUNDARIES (2011)

Excel

Instant Atlas

PREVIOUS MSOA BOUNDARIES (2001)

Excel

Instant Atlas

NB. It is currently not possible to export the map as a picture due to a software issue with the Google Maps background. We advise you to print screen to copy an image to the clipboard.

Tips:

1. - Select a new indicator from the Data box on the left. Select the theme, then indicator and then year to show the data.

2. - To view data just for one borough*, use the filter tool.

3. - The legend settings can be altered by clicking on the pencil icon next to the MSOA tick box within the map legend.

4. - The areas can be ranked in order by clicking at the top of the indicator column of the data table.

Themes included here are Census 2011 Population, Mid-year Estimates, Population by Broad Age, Households, Household composition, Ethnic Group, Country of Birth, Language, Religion, Tenure, Dwelling type, Land Area, Population Density, Births, General Fertility Rate, Deaths, Standardised Mortality Ratio (SMR), Population Turnover Rates (per 1000), Crime (numbers), Crime (rates), House Prices, Commercial property (number), Rateable Value (£ per m2), Floorspace; ('000s m2), Household Income, Household Poverty, County Court Judgements (2005), Qualifications, Economic Activity, Employees, Employment, Claimant Count, Pupil Absence, Early Years Foundation Stage, Key Stage 1, GCSE and Equivalent, Health, Air Emissions, Car or Van availability, Income Deprivation, Central Heating, Incidence of Cancer, Life Expectancy, and Road Casualties.

• The London boroughs are: City of London, Barking and Dagenham, Barnet, Bexley, Brent, Bromley, Camden, Croydon, Ealing, Enfield, Greenwich, Hackney, Hammersmith and Fulham, Haringey, Harrow, Havering, Hillingdon, Hounslow, Islington, Kensington and Chelsea, Kingston upon Thames, Lambeth, Lewisham, Merton, Newham, Redbridge, Richmond upon Thames, Southwark, Sutton, Tower Hamlets, Waltham Forest, Wandsworth, Westminster.

These profiles were created using the most up to date information available at the time of collection (Spring 2014).

You may also be interested in LSOA Atlas and Ward Atlas.

Statistics Canada conducts the Census of Agriculture every five years at the same time as the Census of Population. The most recent Census of Agriculture was on May 15, 2006. The Census of Agriculture collects and disseminates a wide range of data on the agriculture industry such as number and type of farms, farm operator characteristics, business operating arrangements, land management practices, crop areas, numbers of livestock and poultry, farm capital, operating expenses and receipts, and farm machinery and equipment. These data provide a comprehensive picture of the agriculture industry across Canada every five years at the national and provincial levels as well as at lower levels of geography. The Census of Agriculture is the cornerstone of Canada's Agriculture Statistics Program. Census of Agriculture data are an indispensable public and private sector tool for analyzing important changes in the agriculture and food industries; developing, implementing and evaluating agricultural policies and programs such as farm income safety nets and environmental sustainability; and making production, marketing and investment decisions. Statistics Canada uses the data as benchmarks for its regular surveys on crops, livestock and farm finances between census years. This release contains all farm and farm operator data. See People and Products Catalogue for future releases of other agricultural data products.