Visual analytics: Exposing the past, understanding the present, and looking to the future Dan Ariely, founder of The Center for Advanced Hindsight once posted on Facebook, “Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it...” This is especially true in Higher Education as much of the work being done to organize, connect, and analyze big data is happening in the for profit sector. This multimedia presentation (video, photos, and text) has three goals. (1) Discuss how the field visual analytics is tackling the problem of analyzing big data. (2) Explore when visual analytics is superior and inferior to typical statistics. (3) Tactics and tools for Institutional Researchers to use in their everyday work to change data into actionable intelligence.

Categorical scatterplots with R for biologists: a step-by-step guide

Benjamin Petre1, Aurore Coince2, Sophien Kamoun1

1 The Sainsbury Laboratory, Norwich, UK; 2 Earlham Institute, Norwich, UK

Weissgerber and colleagues (2015) recently stated that ‘as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies’. They called for more scatterplot and boxplot representations in scientific papers, which ‘allow readers to critically evaluate continuous data’ (Weissgerber et al., 2015). In the Kamoun Lab at The Sainsbury Laboratory, we recently implemented a protocol to generate categorical scatterplots (Petre et al., 2016; Dagdas et al., 2016). Here we describe the three steps of this protocol: 1) formatting of the data set in a .csv file, 2) execution of the R script to generate the graph, and 3) export of the graph as a .pdf file.

Protocol

• Step 1: format the data set as a .csv file. Store the data in a three-column excel file as shown in Powerpoint slide. The first column ‘Replicate’ indicates the biological replicates. In the example, the month and year during which the replicate was performed is indicated. The second column ‘Condition’ indicates the conditions of the experiment (in the example, a wild type and two mutants called A and B). The third column ‘Value’ contains continuous values. Save the Excel file as a .csv file (File -> Save as -> in ‘File Format’, select .csv). This .csv file is the input file to import in R.

• Step 2: execute the R script (see Notes 1 and 2). Copy the script shown in Powerpoint slide and paste it in the R console. Execute the script. In the dialog box, select the input .csv file from step 1. The categorical scatterplot will appear in a separate window. Dots represent the values for each sample; colors indicate replicates. Boxplots are superimposed; black dots indicate outliers.

• Step 3: save the graph as a .pdf file. Shape the window at your convenience and save the graph as a .pdf file (File -> Save as). See Powerpoint slide for an example.

Notes

• Note 1: install the ggplot2 package. The R script requires the package ‘ggplot2’ to be installed. To install it, Packages & Data -> Package Installer -> enter ‘ggplot2’ in the Package Search space and click on ‘Get List’. Select ‘ggplot2’ in the Package column and click on ‘Install Selected’. Install all dependencies as well.

• Note 2: use a log scale for the y-axis. To use a log scale for the y-axis of the graph, use the command line below in place of command line #7 in the script.

7 Display the graph in a separate window. Dot colors indicate

replicates

graph + geom_boxplot(outlier.colour='black', colour='black') + geom_jitter(aes(col=Replicate)) + scale_y_log10() + theme_bw()

References

Dagdas YF, Belhaj K, Maqbool A, Chaparro-Garcia A, Pandey P, Petre B, et al. (2016) An effector of the Irish potato famine pathogen antagonizes a host autophagy cargo receptor. eLife 5:e10856.

Petre B, Saunders DGO, Sklenar J, Lorrain C, Krasileva KV, Win J, et al. (2016) Heterologous Expression Screens in Nicotiana benthamiana Identify a Candidate Effector of the Wheat Yellow Rust Pathogen that Associates with Processing Bodies. PLoS ONE 11(2):e0149035

Weissgerber TL, Milic NM, Winham SJ, Garovic VD (2015) Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm. PLoS Biol 13(4):e1002128

https://cran.r-project.org/

http://ggplot2.org/

Tables and charts have long been seen as effective ways to convey data. Much attention has been focused on improving charts, following ideas of human perception and brain function. Tables can also be viewed as two-dimensional representations of data, yet it is only fairly recently that we have begun to apply principles of design that aid the communication of information between the author and reader. In this study, we collated guidelines for the design of data and statistical tables. These guidelines fall under three principles: aiding comparisons, reducing visual clutter, and increasing readability. We surveyed tables published in recent issues of 43 journals in the fields of ecology and evolutionary biology for their adherence to these three principles, as well as author guidelines on journal publisher websites. We found that most of the over 1,000 tables we sampled had no heavy grid lines and little visual clutter. They were also easy to read, with clear headers and horizontal orient..., Once we had established the above principles of table design, we assessed their use in issues of 43 widely read ecology and evolution journals (SI 2). Between January and July 2022, we reviewed the tables in the most recent issue published by these journals. For journals without issues (such as Annual Review of Ecology, Evolution, and Systematics, or Biological Conservation), we examined the tables in issues published in a single month or in the entire most recent volume if few papers were published in that journal on a monthly basis. We reviewed only articles in a traditionally typeset format and published as a PDF or in print. We did not examine the tables in online versions of articles. Having identified all tables for review, we assessed whether these tables followed the above-described best practice principles for table design and, if not, we noted the way in which these tables failed to meet the outlined guidelines. We initially both reviewed the same 10 tables to ensure that we a..., , # Design of tables for the presentation and communication of data in ecological and evolutionary biology

Once we had established the above principles of table design, we assessed their use in issues of 43 widely read ecology and evolution journals (SI 2). Between January and July 2022, we reviewed the tables in the most recent issue published by these journals. For journals without issues (such as Annual Review of Ecology, Evolution, and Systematics, or Biological Conservation), we examined the tables in issues published in a single month or in the entire most recent volume if few papers were published in that journal on a monthly basis. We reviewed only articles in a traditionally typeset format and published as a PDF or in print. We did not examine the tables in online versions of articles.

Having identified all tables for review, we assessed whether these tables followed the above-described best practice principles for table design and, if not, we noted the way in which these ...

The PRIEST study used patient data from the early phases of the COVID-19 pandemic. The PRIEST study provided descriptive statistics of UK patients with suspected COVID-19 in an emergency department cohort, analysis of existing triage tools, and derivation and validation of a COVID-19 specific tool for adults with suspected COVID-19. For more details please go to the study website:https://www.sheffield.ac.uk/scharr/research/centres/cure/priestFiles contained in PRIEST study data repository Main files include:PRIEST.csv dataset contains 22445 observations and 119 variables. Data include initial presentation and follow-up, one row per participant.PRIEST_variables.csv contains variable names, values and brief description.Additional files include:Follow-up v4.0 PDF - Blank 30-day follow-up data collection toolPandemic Respiratory Infection Form v7 PDF - Blank baseline data collection toolPRIEST protocol v11.0_17Aug20 PDF - Study protocolPRIEST_SAP_v1.0_19jun20 PDF - Statistical analysis planThe PRIEST data sharing plan follows a controlled access model as described in Good Practice Principles for Sharing Individual Participant Data from Publicly Funded Clinical Trials. Data sharing requests should be emailed to priest-study@sheffield.ac.uk. Data sharing requests will be considered carefully as to whether it is necessary to fulfil the purpose of the data sharing request. For approval of a data sharing request an approved ethical review and study protocol must be provided. The PRIEST study was approved by NRES Committee North West - Haydock. REC reference: 12/NW/0303

This dataset FDI positions main aggregates, BMD4 is updated every quarter and includes annual aggregate Foreign Direct Investment (FDI) positions for OECD member countries and for non-OECD G20 countries (Argentina, Brazil, China, India, Indonesia, Saudi Arabia and South Africa), which are included in International Investment Position (IIP) accounts.

FDI positions record the total level of direct investment at a given point in time, usually the end of a quarter or of a year.

In this dataset, FDI positions are presented on two different basis:

• the asset/liability presentation: FDI statistics are organised according to whether the investment relates to an asset or a liability for the country compiling the statistics. The asset/liability presentation does not show the direction of influence as the directional presentation does.
• the directional presentation: FDI statistics are organised according to the direction of the investment for the reporting economy—either outward or inward.

For more details on the difference between the two presentations, see the OECD note Implementing latest international standards-Asset liability versus directional presentation

FDI positions aggregates in this dataset are measured in USD millions, in millions of national currency and as a share of GDP.

In 2014, many countries implemented the latest international standards for Foreign Direct Investment (FDI) statistics:

• the OECD’s Benchmark Definition of FDI, 4th edition (BMD4); and
• the IMF’s Balance of Payments and International Investment Position Manual, 6th edition (BPM6)

This OECD database was launched in March 2015 which includes the data series reported by national experts according to BMD4. The data are for the most part based on balance of payments statistics published by Central Banks and Statistical Offices following the recommendations of the IMF’s BPM6 and the OECD’s BMD4. However, some of the data relate to other sources such as notifications or approvals.

Historical and unrevised series of FDI positions aggregates under the previous BMD3 methodology can be accessed in the archived dataset FDI series of BOP and IIP aggregates

Analysis of ‘2018 NYC Open Data Plan: FOIL Summary Statistics’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/2405440c-17b5-49e0-95fb-c561735a2a2e on 13 November 2021.

--- Dataset description provided by original source is as follows ---

Local Law 7 of 2016 requires agencies to “review responses to freedom of information law [FOIL] requests that include the release of data to determine if such responses consist of or include public data sets that have not yet been included on the single web portal or the inclusion” on the Open Data Portal. Additionally, each City agency shall disclose “the total number, since the last update, of such agency’s freedom of information law responses that included the release of data, the total number of such responses determined to consist of or include a public data set that had not yet been included on the single web portal and the name of such public data set, where applicable, and the total number of such responses that resulted in voluntarily disclosed information being made accessible through the single web portal.”

See the itemized public datasets used to respond to FOIL requests not yet published on the Open Data Portal in FY2018 here: https://data.cityofnewyork.us/City-Government/2018-Open-Data-Plan-FOIL-Datasets/sjdi-a6us

See the 2018 Open Data for All Report and Open Data Plan here: https://opendata.cityofnewyork.us/wp-content/uploads/2018/09/2018-NYC-OD4A-report.pdf

--- Original source retains full ownership of the source dataset ---

This 6MB download is a zip file containing 5 pdf documents and 2 xlsx spreadsheets. Presentation on COVID-19 and the potential impacts on employment

May 2020Waka Kotahi wants to better understand the potential implications of the COVID-19 downturn on the land transport system, particularly the potential impacts on regional economies and communities.

To do this, in May 2020 Waka Kotahi commissioned Martin Jenkins and Infometrics to consider the potential impacts of COVID-19 on New Zealand’s economy and demographics, as these are two key drivers of transport demand. In addition to providing a scan of national and international COVID-19 trends, the research involved modelling the economic impacts of three of the Treasury’s COVID-19 scenarios, to a regional scale, to help us understand where the impacts might be greatest.

Waka Kotahi studied this modelling by comparing the percentage difference in employment forecasts from the Treasury’s three COVID-19 scenarios compared to the business as usual scenario.

The source tables from the modelling (Tables 1-40), and the percentage difference in employment forecasts (Tables 41-43), are available as spreadsheets.

Arataki - potential impacts of COVID-19 Final Report

Employment modelling - interactive dashboard

The modelling produced employment forecasts for each region and district over three time periods – 2021, 2025 and 2031. In May 2020, the forecasts for 2021 carried greater certainty as they reflected the impacts of current events, such as border restrictions, reduction in international visitors and students etc. The 2025 and 2031 forecasts were less certain because of the potential for significant shifts in the socio-economic situation over the intervening years. While these later forecasts were useful in helping to understand the relative scale and duration of potential COVID-19 related impacts around the country, they needed to be treated with care recognising the higher levels of uncertainty.

The May 2020 research suggested that the ‘slow recovery scenario’ (Treasury’s scenario 5) was the most likely due to continuing high levels of uncertainty regarding global efforts to manage the pandemic (and the duration and scale of the resulting economic downturn).

The updates to Arataki V2 were framed around the ‘Slower Recovery Scenario’, as that scenario remained the most closely aligned with the unfolding impacts of COVID-19 in New Zealand and globally at that time.

Find out more about Arataki, our 10-year plan for the land transport system

May 2021The May 2021 update to employment modelling used to inform Arataki Version 2 is now available. Employment modelling dashboard - updated 2021Arataki used the May 2020 information to compare how various regions and industries might be impacted by COVID-19. Almost a year later, it is clear that New Zealand fared better than forecast in May 2020.Waka Kotahi therefore commissioned an update to the projections through a high-level review of:the original projections for 2020/21 against performancethe implications of the most recent global (eg International monetary fund world economic Outlook) and national economic forecasts (eg Treasury half year economic and fiscal update)The treasury updated its scenarios in its December half year fiscal and economic update (HYEFU) and these new scenarios have been used for the revised projections.Considerable uncertainty remains about the potential scale and duration of the COVID-19 downturn, for example with regards to the duration of border restrictions, update of immunisation programmes. The updated analysis provides us with additional information regarding which sectors and parts of the country are likely to be most impacted. We continue to monitor the situation and keep up to date with other cross-Government scenario development and COVID-19 related work. The updated modelling has produced employment forecasts for each region and district over three time periods - 2022, 2025, 2031.The 2022 forecasts carry greater certainty as they reflect the impacts of current events. The 2025 and 2031 forecasts are less certain because of the potential for significant shifts over that time.

Data reuse caveats: as per license.

Additionally, please read / use this data in conjunction with the Infometrics and Martin Jenkins reports, to understand the uncertainties and assumptions involved in modelling the potential impacts of COVID-19.

COVID-19’s effect on industry and regional economic outcomes for NZ Transport Agency [PDF 620 KB]

Data quality statement: while the modelling undertaken is high quality, it represents two point-in-time analyses undertaken during a period of considerable uncertainty. This uncertainty comes from several factors relating to the COVID-19 pandemic, including:

a lack of clarity about the size of the global downturn and how quickly the international economy might recover differing views about the ability of the New Zealand economy to bounce back from the significant job losses that are occurring and how much of a structural change in the economy is required the possibility of a further wave of COVID-19 cases within New Zealand that might require a return to Alert Levels 3 or 4.

While high levels of uncertainty remain around the scale of impacts from the pandemic, particularly in coming years, the modelling is useful in indicating the direction of travel and the relative scale of impacts in different parts of the country.

Data quality caveats: as noted above, there is considerable uncertainty about the potential scale and duration of the COVID-19 downturn. Please treat the specific results of the modelling carefully, particularly in the forecasts to later years (2025, 2031), given the potential for significant shifts in New Zealand's socio-economic situation before then.

As such, please use the modelling results as a guide to the potential scale of the impacts of the downturn in different locations, rather than as a precise assessment of impacts over the coming decade.

This dataset FDI flows main aggregates, BMD4 is updated every quarter and includes annual and quarterly aggregate Foreign Direct Investment (FDI) flows for OECD member countries and for non-OECD G20 countries (Argentina, Brazil, China, India, Indonesia, Saudi Arabia and South Africa), which are included in Balance of Payments (BOP) accounts.

FDI flows record the value of cross-border transactions related to direct investment during a given period of time, usually a quarter or a year, and consist of equity transactions, reinvestment of earnings, and intercompany debt transactions.

In this dataset, FDI flows are presented on two different basis:

• the asset/liability presentation: FDI statistics are organised according to whether the investment relates to an asset or a liability for the country compiling the statistics. The asset/liability presentation does not show the direction of influence as the directional presentation does.
• the directional presentation: FDI statistics are organised according to the direction of the investment for the reporting economy—either outward or inward.

For more details on the difference between the two presentations, see the OECD note Implementing latest international standards-Asset liability versus directional presentation

FDI flows aggregates in this dataset are measured in USD millions, in millions of national currency and as a share of GDP.

In 2014, many countries implemented the latest international standards for Foreign Direct Investment (FDI) statistics:

• the OECD’s Benchmark Definition of FDI, 4th edition (BMD4); and
• the IMF’s Balance of Payments and International Investment Position Manual, 6th edition (BPM6)

This OECD database was launched in March 2015 which includes the data series reported by national experts according to BMD4. The data are for the most part based on balance of payments statistics published by Central Banks and Statistical Offices following the recommendations of the IMF’s BPM6 and the OECD’s BMD4. However, some of the data relate to other sources such as notifications or approvals.

Historical and unrevised series of FDI flows aggregates under the previous BMD3 methodology can be accessed in the archived dataset FDI series of BOP and IIP aggregates

Labour force survey (LFS) Purpose and short description The Labour Force Survey (LFS) is a socio-economic household sample survey. Its main objective is to classify the working age population (15 and older) into three groups (employed, unemployed and inactive persons) and to provide descriptive and explanatory data on every category. This survey is also carried out in the other EU Member States and is coordinated by Eurostat, the statistical office of the European Union. In Belgium, the LFS is organised by Statbel. The objective is to obtain comparable information at European level, in particular as regards employment and unemployment rates as defined by the International Labour Office (ILO), but also to collect and disseminate data that are otherwise not available, for example about the mobility of workers, the reasons for working part-time, the various forms of part-time employment, the occupation, the educational level of the working age population, ... . Survey population Members of private households aged 15 or older. Sample frame Demographic data from the National Register. Data collection method and sample size Data are collected through face-to-face interviews. Since 2017, there have been three (shorter) follow-up surveys to which households respond online or by telephone. Households with only inactive persons older than 64 can also be interviewed by telephone. Every year, around 47,000 households receive a letter asking them to take part in this survey. Response rate The response rate is above 75%. Periodicity Quarterly Release calendar Results availability: around 3 months after the end of the reference period. Forms Labour Force Survey 2020 (PDF, 541 Kb) Labour Force Survey 2021 (PDF, 1 Mb) Definitions Unemployed (ILO): According to the criteria of the International Labour Office, the unemployed include all people aged 15 years and over who: a) were without work during the reference week b) were available for work, i.e. were available for paid employment or self-employment within two weeks after the reference week c) were actively seeking work, i.e. had taken specific steps during the last four weeks including the reference week to seek paid employment or self-employment, or who had found a job to start within a maximum period of three months. Employed population (ILO): The employed comprise all people aged 15 and over who during the reference week performed some work for at least one hour for wage or salary, or for profit, or who had a job but were temporarily absent. For example, one can be temporarily absent for holidays, illness, technical or economic reasons (temporary unemployment),....Family workers are also included in the category ‘employed’. The employed are divided into three groups according to their professional status: Education level (3 classes): Low-skilled people are people who have at most a diploma of lower secondary education. Medium-skilled people are people who obtained a diploma of upper secondary education but not of higher education. Highly-skilled people have a diploma of higher education. Early leavers from education and training: the percentage of people aged 18 to 24 who did not complete upper secondary education and who is no longer involved in any form of education or training. Metadata Employment, unemployment, labour market (NL-FR) Labour force survey (LFS) (NL-FR) Survey methodology Modifications to the Labour Force Survey (LFS) in 2021 LFS: Methodological improvements to the Labour Force Survey 2017 (PDF, 99 Kb) LFS: Presentation of the survey until 2016 (NL-FR) LFS: Presentation of the survey from 2017 (NL-FR) Regulations Royal Decree of 10 January 1999 on the organisation of a labour force sample survey (NL-FR) Royal decree amending the royal decree of 10 January 1999 on the organisation of a labour force sample survey (NL-FR)

This file contains COVID-19 death counts and rates by month and year of death, jurisdiction of residence (U.S., HHS Region) and demographic characteristics (sex, age, race and Hispanic origin, and age/race and Hispanic origin). United States death counts and rates include the 50 states, plus the District of Columbia.

Deaths with confirmed or presumed COVID-19, coded to ICD–10 code U07.1. Number of deaths reported in this file are the total number of COVID-19 deaths received and coded as of the date of analysis and may not represent all deaths that occurred in that period. Counts of deaths occurring before or after the reporting period are not included in the file.

Data during recent periods are incomplete because of the lag in time between when the death occurred and when the death certificate is completed, submitted to NCHS and processed for reporting purposes. This delay can range from 1 week to 8 weeks or more, depending on the jurisdiction and cause of death.

Death counts should not be compared across jurisdictions. Data timeliness varies by state. Some states report deaths on a daily basis, while other states report deaths weekly or monthly.

The ten (10) United States Department of Health and Human Services (HHS) regions include the following jurisdictions. Region 1: Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont; Region 2: New Jersey, New York; Region 3: Delaware, District of Columbia, Maryland, Pennsylvania, Virginia, West Virginia; Region 4: Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, Tennessee; Region 5: Illinois, Indiana, Michigan, Minnesota, Ohio, Wisconsin; Region 6: Arkansas, Louisiana, New Mexico, Oklahoma, Texas; Region 7: Iowa, Kansas, Missouri, Nebraska; Region 8: Colorado, Montana, North Dakota, South Dakota, Utah, Wyoming; Region 9: Arizona, California, Hawaii, Nevada; Region 10: Alaska, Idaho, Oregon, Washington.

Rates were calculated using the population estimates for 2021, which are estimated as of July 1, 2021 based on the Blended Base produced by the US Census Bureau in lieu of the April 1, 2020 decennial population count. The Blended Base consists of the blend of Vintage 2020 postcensal population estimates, 2020 Demographic Analysis Estimates, and 2020 Census PL 94-171 Redistricting File (see https://www2.census.gov/programs-surveys/popest/technical-documentation/methodology/2020-2021/methods-statement-v2021.pdf).

Rate are based on deaths occurring in the specified week and are age-adjusted to the 2000 standard population using the direct method (see https://www.cdc.gov/nchs/data/nvsr/nvsr70/nvsr70-08-508.pdf). These rates differ from annual age-adjusted rates, typically presented in NCHS publications based on a full year of data and annualized weekly age-adjusted rates which have been adjusted to allow comparison with annual rates. Annualization rates presents deaths per year per 100,000 population that would be expected in a year if the observed period specific (weekly) rate prevailed for a full year.

Sub-national death counts between 1-9 are suppressed in accordance with NCHS data confidentiality standards. Rates based on death counts less than 20 are suppressed in accordance with NCHS standards of reliability as specified in NCHS Data Presentation Standards for Proportions (available from: https://www.cdc.gov/nchs/data/series/sr_02/sr02_175.pdf.).

This is the additional material for the Paper "IT Managers’ Perspective on Technical Debt Management" submitted to the Journal of Systems and Software.

The folder contains the following files:

• Consent form - blinded.pdf:
  The consent form was used to gather the participants' consent to participate in this study.
• PreliminarySurvey.pdf:
  The survey was sent to all participants in advance to gather basic statistical data.
• ParticipantsOverview.pdf
  The overview of all participants was extracted from the preliminary survey. For anonymity reasons, we can not provide the country/language of the participants.
• InterviewGuide.pdf
  The interview guide was followed by the interviewers, including all questions and the rationale for questions added before the 2nd iteration.
• CodeSystem.xlsx
  All Codes were gathered from the interviews with a short explanation (in Excel to use the grouping function for a better overview).
• AnalyticMemos_Themes.pdf
  All free memos were created during the study, including all themes that led to the main concerns described in the paper.
• Focusgroup-discussion_Themes_slides.pdf
  The slides were created for the focus group discussion. The participants were introduced to the "main concerns" and asked to comment on these findings.
• V4C_model.drawio
  This is the original drawio file of our V4C model, including all diagrams from the paper. Additionally, we provide the complete version of the visibility diagram, which had to be reduced in the paper due to the page limitation. Also, the file contains one more diagram with another example of the model's usage.
• V4Cmodel.pdf, V4Cmodel_example.pdf, VisibilityOfCausesConsequences.pdf; VisibilityCycles.pdf
  All diagrams of the paper are in single PDF files.
• V4CTDmodel+example.pptx
  A short presentation of the model and an example of its usage in PowerPoint format to immediately use.

The Hierarchical Representation of UK Statistical Geographies shows all UK statistical geographies within their geography groups and geographical areas, as at April 2020. The pdf is scaled to A3 paper size. (File Size - 5 MB)

Labour force survey (LFS) Purpose and short description The Labour Force Survey (LFS) is a socio-economic household sample survey. Its main objective is to classify the working age population (15 and older) into three groups (employed, unemployed and inactive persons) and to provide descriptive and explanatory data on every category. This survey is also carried out in the other EU Member States and is coordinated by Eurostat, the statistical office of the European Union. In Belgium, the LFS is organised by Statbel. The objective is to obtain comparable information at European level, in particular as regards employment and unemployment rates as defined by the International Labour Office (ILO), but also to collect and disseminate data that are otherwise not available, for example about the mobility of workers, the reasons for working part-time, the various forms of part-time employment, the occupation, the educational level of the working age population, ... . Survey population Members of private households aged 15 or older. Sample frame Demographic data from the National Register. Data collection method and sample size Data are collected through face-to-face interviews. Since 2017, there have been three (shorter) follow-up surveys to which households respond online or by telephone. Households with only inactive persons older than 64 can also be interviewed by telephone. Every year, around 47,000 households receive a letter asking them to take part in this survey. Response rate The response rate is above 75%. Periodicity Quarterly Release calendar Results availability: around 3 months after the end of the reference period. Forms Labour Force Survey 2020 (PDF, 541 Kb) Labour Force Survey 2021 (PDF, 1 Mb) Metadata Employment, unemployment, labour market (NL-FR) Labour force survey (LFS) (NL-FR) Survey methodology Modifications to the Labour Force Survey (LFS) in 2021 LFS: Methodological improvements to the Labour Force Survey 2017 (PDF, 99 Kb) LFS: Presentation of the survey until 2016 (NL-FR) LFS: Presentation of the survey from 2017 (NL-FR) Regulations Royal Decree of 10 January 1999 on the organisation of a labour force sample survey (NL-FR) Royal decree amending the royal decree of 10 January 1999 on the organisation of a labour force sample survey (NL-FR)

This layer contains a unique geographic identifier (GEO_ID_TRT) for each tract group that is the key field for the data from censuses and surveys such as Decennial Census, Economic Census, American Community Survey, and the Population Estimates Program. Data from many of the Census Bureau’s surveys and censuses, are available at the Census Bureau’s public data dissemination website (https://data.census.gov/). All original TIGER/Line shapefiles and geodatabases with demographic data are available atThe TIGER/Line Shapefiles are extracts of selected geographic and cartographic information from the Census Bureau's Master Address File (MAF)/Topologically Integrated Geographic Encoding and Referencing (TIGER) Database (MTDB). The shapefiles include information for the fifty states, the District of Columbia, Puerto Rico, and the Island areas (American Samoa, the Commonwealth of the Northern Mariana Islands, Guam, and the United States Virgin Islands). The shapefiles include polygon boundaries of geographic areas and features, linear features including roads and hydrography, and point features. These shapefiles do not contain any sensitive data or confidential data protected by Title 13 of the U.S.C.Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and are reviewed and updated by local participants prior to each decennial census. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of decennial census data. Census tracts generally have a population size of 1,200 to 8,000 people with an optimum size of 4,000 people. The spatial size of census tracts varies widely depending on the density of settlement. Ideally, census tract boundaries remain stable over time to facilitate statistical comparisons from census to census. However, physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, significant changes in population may result in splitting or combining census tracts. Census tract boundaries generally follow visible and identifiable features. Census tract boundaries may follow legal boundaries. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. Census Tract Codes and Numbers—Census tract numbers have up to a 4-character basic number and may have an optional 2-character suffix, for example, 1457.02. The Census Bureau uses suffixes to help identify census tract changes for comparison purposes. Full documentation: https://www2.census.gov/geo/pdfs/maps-data/data/tiger/tgrshp2020/TGRSHP2020_TechDoc.pdf

Summary statistics data generated in Ferrari et al, 2014, Lancet Neurol (PMID: 24943344)The International FTD-Genetics Consortium (IFGC) shares the summary results data to allow other researchers to explore variants and/or loci for hypothesis driven work. The data provides information on ~ 6M markers and includes information about: marker – trait – allele 1 and 2 – OR or Beta – standard error – p-value, chromosome and bp position.To prevent identification of individuals, allele frequency data are not released.The data consists of the summary statistics generated during discovery phase (phase I) of the study including: bvFTD (n=1377 vs 2754 ctrls) AND/OR SD (n=308 vs 616 ctrls) AND/OR PNFA (n=269 vs 538 ctrls) AND/OR FTD-MND (n=200 vs 400 ctrls) AND/OR subtypes meta-analysis.Note:1. The IFGC requests to be included among co-authors in publications that might result from the use of this data as “The International FTD-Genetics Consortium (IFGC)” following Pubmed guidelines where Consortia or working group authors shall be listed on PubMed as collaborators rather than authors, where collaborator names are searchable on PubMed in the same way as authors. The acknowledgments associated with the IFGC as well as the IFGC members are provided as separate pdf document, together with the summary statistics;2. Publications (including but not limited to manuscripts, presentation, patent, grant) based on this IFGC’s dataset shall include the citation of the original work (Ferrari et al, 2014, Lancet Neurol, PMID: 24943344) and add the following to the acknowledgement section: “We thank the International FTD-Genetics Consortium (IFGC) for summary data”.

On this page, the Cologne police provide you with the 2015 police crime statistics for the city of Cologne as raw data.

You can access a graphical presentation and other statistics here:
http://www.police.nrw.de/koeln/category_60.html
www.koeln.polizei.nrw.de

Source: Police North Rhine-Westphalia "http://www.polizei.nrw.de/koeln/index.html">http://www.polizei.nrw.de/koeln/index.html

Age structure:

Children up to the age of 14 (the 14th birthday counts as the key date for criminal responsibility)

Young people aged 15 to under 18

Adolescents aged 18 to under 21

Adults aged 21 to under 60

Seniors 60+

Labour force survey (LFS) Purpose and short description The Labour Force Survey (LFS) is a socio-economic household sample survey. Its main objective is to classify the working age population (15 and older) into three groups (employed, unemployed and inactive persons) and to provide descriptive and explanatory data on every category. This survey is also carried out in the other EU Member States and is coordinated by Eurostat, the statistical office of the European Union. In Belgium, the LFS is organised by Statbel. The objective is to obtain comparable information at European level, in particular as regards employment and unemployment rates as defined by the International Labour Office (ILO), but also to collect and disseminate data that are otherwise not available, for example about the mobility of workers, the reasons for working part-time, the various forms of part-time employment, the occupation, the educational level of the working age population, ... . Survey population Members of private households aged 15 or older. Sample frame Demographic data from the National Register. Data collection method and sample size Data are collected through face-to-face interviews. Since 2017, there have been three (shorter) follow-up surveys to which households respond online or by telephone. Households with only inactive persons older than 64 can also be interviewed by telephone. Every year, around 47,000 households receive a letter asking them to take part in this survey. Response rate The response rate is above 75%. Periodicity Quarterly Release calendar Results availability: around 3 months after the end of the reference period. Forms Labour Force Survey 2020 (PDF, 541 Kb) Labour Force Survey 2021 (PDF, 1 Mb) Definitions Employed population (ILO): The employed persons are persons aged 15 or older who during the reference week performed work during at least one hour for wage or salary or for profit; or those who had a job but who were temporarily not at work during the reference period. The family workers are also included. The employed persons are divided into three groups according to their professional situation: Paid employment: All persons aged 15 or older who, during the reference week, performed work during at least one hour for wage or salary in cash or in kind (with or without formal contract), or who were temporarily not at work (due to sickness, maternity leave, holidays, social conflicts, bad weather or for other reasons) and had a formal attachment to their job. Self-employment: All persons who do not work for an employer and who performed work during at least one hour for profit during the reference week or were temporarily not at work. This category comprises self-employed workers (with no staff), employers (with staff) and unpaid helpers. Low-skilled people are people who have at best a lower secondary education diploma. Medium-skilled people have obtained an upper secondary education diploma, but no higher education diploma. High-skilled people have a higher education diploma. Metadata Employment, unemployment, labour market (NL-FR) Labour force survey (LFS) (NL-FR) Survey methodology Modifications to the Labour Force Survey (LFS) in 2021 LFS: Methodological improvements to the Labour Force Survey 2017 (PDF, 99 Kb) LFS: Presentation of the survey until 2016 (NL-FR) LFS: Presentation of the survey from 2017 (NL-FR) Regulations Royal Decree of 10 January 1999 on the organisation of a labour force sample survey (NL-FR) Royal decree amending the royal decree of 10 January 1999 on the organisation of a labour force sample survey (NL-FR)

Labour force survey (LFS) Purpose and short description The Labour Force Survey (LFS) is a socio-economic household sample survey. Its main objective is to classify the working age population (15 and older) into three groups (employed, unemployed and inactive persons) and to provide descriptive and explanatory data on every category. This survey is also carried out in the other EU Member States and is coordinated by Eurostat, the statistical office of the European Union. In Belgium, the LFS is organised by Statbel. The objective is to obtain comparable information at European level, in particular as regards employment and unemployment rates as defined by the International Labour Office (ILO), but also to collect and disseminate data that are otherwise not available, for example about the mobility of workers, the reasons for working part-time, the various forms of part-time employment, the occupation, the educational level of the working age population, ... . Survey population Members of private households aged 15 or older. Sample frame Demographic data from the National Register. Data collection method and sample size Data are collected through face-to-face interviews. Since 2017, there have been three (shorter) follow-up surveys to which households respond online or by telephone. Households with only inactive persons older than 64 can also be interviewed by telephone. Every year, around 47,000 households receive a letter asking them to take part in this survey. Response rate The response rate is above 75%. Periodicity Quarterly Release calendar Results availability: around 3 months after the end of the reference period. Forms Labour Force Survey 2020 (PDF, 541 Kb) Labour Force Survey 2021 (PDF, 1 Mb) Definitions Unemployed (ILO): According to the criteria of the International Labour Office, the unemployed include all people aged 15 years and over who: a) were without work during the reference week b) were available for work, i.e. were available for paid employment or self-employment within two weeks after the reference week c) were actively seeking work, i.e. had taken specific steps during the last four weeks including the reference week to seek paid employment or self-employment, or who had found a job to start within a maximum period of three months. Employed population (ILO): The employed comprise all people aged 15 and over who during the reference week performed some work for at least one hour for wage or salary, or for profit, or who had a job but were temporarily absent. For example, one can be temporarily absent for holidays, illness, technical or economic reasons (temporary unemployment),....Family workers are also included in the category ‘employed’. The employed are divided into three groups according to their professional status: Education level (3 classes): Low-skilled people are people who have at most a diploma of lower secondary education. Medium-skilled people are people who obtained a diploma of upper secondary education but not of higher education. Highly-skilled people have a diploma of higher education. Early leavers from education and training: the percentage of people aged 18 to 24 who did not complete upper secondary education and who is no longer involved in any form of education or training. Metadata Employment, unemployment, labour market (NL-FR) Labour force survey (LFS) (NL-FR) Survey methodology Modifications to the Labour Force Survey (LFS) in 2021 LFS: Methodological improvements to the Labour Force Survey 2017 (PDF, 99 Kb) LFS: Presentation of the survey until 2016 (NL-FR) LFS: Presentation of the survey from 2017 (NL-FR) Regulations Royal Decree of 10 January 1999 on the organisation of a labour force sample survey (NL-FR) Royal decree amending the royal decree of 10 January 1999 on the organisation of a labour force sample survey (NL-FR)

On this page, the Cologne police provide you with the 2015 police crime statistics for the Cologne city area as raw data.

You can access a graphical presentation and further statistics here:
http://www.polizei.nrw.de/koeln/klasse_60.html
www.koeln.polizei.nrw.de

Source: Police North Rhine-Westphalia "http://www.polizei.nrw.de/koeln/index.html">http://www.polizei.nrw.de/koeln/index.html

Age structure:

Children up to the age of 14 (the 14th birthday counts as the cut-off date for criminal responsibility)

Young people aged 15 to under 18

Adolescents aged 18 to under 21

Adults aged 21 to under 60

Seniors aged 60+

Analysis of ‘Statistical News, 1924-2001 ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/4a2743d2-e23c-44b6-b906-7a0b67991790-stadt-zurich on 17 January 2022.

--- Dataset description provided by original source is as follows ---

The Statistical News is a collection of individual essays on various topics of Statistics City of Zurich published annually from 1924 to 2001. The dataset contains all statistical messages divided into the individual articles as PDF.

--- Original source retains full ownership of the source dataset ---