Excel file containing additional data too large to fit in a PDF, CUT&RUN–RNAseq merge analyses.

For any questions about this data please email me at jacob@crimedatatool.com. If you use this data, please cite it.Version 4 release notes:Adds data for 2018Version 3 release notes:Adds data in the following formats: Excel.Changes project name to avoid confusing this data for the ones done by NACJD.Version 2 release notes:Adds data for 2017.Adds a "number_of_months_reported" variable which says how many months of the year the agency reported data.Property Stolen and Recovered is a Uniform Crime Reporting (UCR) Program data set with information on the number of offenses (crimes included are murder, rape, robbery, burglary, theft/larceny, and motor vehicle theft), the value of the offense, and subcategories of the offense (e.g. for robbery it is broken down into subcategories including highway robbery, bank robbery, gas station robbery). The majority of the data relates to theft. Theft is divided into subcategories of theft such as shoplifting, theft of bicycle, theft from building, and purse snatching. For a number of items stolen (e.g. money, jewelry and previous metals, guns), the value of property stolen and and the value for property recovered is provided. This data set is also referred to as the Supplement to Return A (Offenses Known and Reported). All the data was received directly from the FBI as text or .DTA files. I created a setup file based on the documentation provided by the FBI and read the data into R using the package asciiSetupReader. All work to clean the data and save it in various file formats was also done in R. For the R code used to clean this data, see here: https://github.com/jacobkap/crime_data. The Word document file available for download is the guidebook the FBI provided with the raw data which I used to create the setup file to read in data.There may be inaccuracies in the data, particularly in the group of columns starting with "auto." To reduce (but certainly not eliminate) data errors, I replaced the following values with NA for the group of columns beginning with "offenses" or "auto" as they are common data entry error values (e.g. are larger than the agency's population, are much larger than other crimes or months in same agency): 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000, 99942. This cleaning was NOT done on the columns starting with "value."For every numeric column I replaced negative indicator values (e.g. "j" for -1) with the negative number they are supposed to be. These negative number indicators are not included in the FBI's codebook for this data but are present in the data. I used the values in the FBI's codebook for the Offenses Known and Clearances by Arrest data.To make it easier to merge with other data, I merged this data with the Law Enforcement Agency Identifiers Crosswalk (LEAIC) data. The data from the LEAIC add FIPS (state, county, and place) and agency type/subtype. If an agency has used a different FIPS code in the past, check to make sure the FIPS code is the same as in this data.

Version 5 release notes:
Removes support for SPSS and Excel data.Changes the crimes that are stored in each file. There are more files now with fewer crimes per file. The files and their included crimes have been updated below.
Adds in agencies that report 0 months of the year.Adds a column that indicates the number of months reported. This is generated summing up the number of unique months an agency reports data for. Note that this indicates the number of months an agency reported arrests for ANY crime. They may not necessarily report every crime every month. Agencies that did not report a crime with have a value of NA for every arrest column for that crime.Removes data on runaways.
Version 4 release notes:
Changes column names from "poss_coke" and "sale_coke" to "poss_heroin_coke" and "sale_heroin_coke" to clearly indicate that these column includes the sale of heroin as well as similar opiates such as morphine, codeine, and opium. Also changes column names for the narcotic columns to indicate that they are only for synthetic narcotics.
Version 3 release notes:
Add data for 2016.Order rows by year (descending) and ORI.Version 2 release notes:
Fix bug where Philadelphia Police Department had incorrect FIPS county code.
The Arrests by Age, Sex, and Race data is an FBI data set that is part of the annual Uniform Crime Reporting (UCR) Program data. This data contains highly granular data on the number of people arrested for a variety of crimes (see below for a full list of included crimes). The data sets here combine data from the years 1980-2015 into a single file. These files are quite large and may take some time to load.
All the data was downloaded from NACJD as ASCII+SPSS Setup files and read into R using the package asciiSetupReader. All work to clean the data and save it in various file formats was also done in R. For the R code used to clean this data, see here. https://github.com/jacobkap/crime_data. If you have any questions, comments, or suggestions please contact me at jkkaplan6@gmail.com.

I did not make any changes to the data other than the following. When an arrest column has a value of "None/not reported", I change that value to zero. This makes the (possible incorrect) assumption that these values represent zero crimes reported. The original data does not have a value when the agency reports zero arrests other than "None/not reported." In other words, this data does not differentiate between real zeros and missing values. Some agencies also incorrectly report the following numbers of arrests which I change to NA: 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000, 99999, 99998.

To reduce file size and make the data more manageable, all of the data is aggregated yearly. All of the data is in agency-year units such that every row indicates an agency in a given year. Columns are crime-arrest category units. For example, If you choose the data set that includes murder, you would have rows for each agency-year and columns with the number of people arrests for murder. The ASR data breaks down arrests by age and gender (e.g. Male aged 15, Male aged 18). They also provide the number of adults or juveniles arrested by race. Because most agencies and years do not report the arrestee's ethnicity (Hispanic or not Hispanic) or juvenile outcomes (e.g. referred to adult court, referred to welfare agency), I do not include these columns.

To make it easier to merge with other data, I merged this data with the Law Enforcement Agency Identifiers Crosswalk (LEAIC) data. The data from the LEAIC add FIPS (state, county, and place) and agency type/subtype. Please note that some of the FIPS codes have leading zeros and if you open it in Excel it will automatically delete those leading zeros.

I created 9 arrest categories myself. The categories are:
Total Male JuvenileTotal Female JuvenileTotal Male AdultTotal Female AdultTotal MaleTotal FemaleTotal JuvenileTotal AdultTotal ArrestsAll of these categories are based on the sums of the sex-age categories (e.g. Male under 10, Female aged 22) rather than using the provided age-race categories (e.g. adult Black, juvenile Asian). As not all agencies report the race data, my method is more accurate. These categories also make up the data in the "simple" version of the data. The "simple" file only includes the above 9 columns as the arrest data (all other columns in the data are just agency identifier columns). Because this "simple" data set need fewer columns, I include all offenses.

As the arrest data is very granular, and each category of arrest is its own column, there are dozens of columns per crime. To keep the data somewhat manageable, there are nine different files, eight which contain different crimes and the "simple" file. Each file contains the data for all years. The eight categories each have crimes belonging to a major crime category and do not overlap in crimes other than with the index offenses. Please note that the crime names provided below are not the same as the column names in the data. Due to Stata limiting column names to 32 characters maximum, I have abbreviated the crime names in the data. The files and their included crimes are:

Index Crimes
MurderRapeRobberyAggravated AssaultBurglaryTheftMotor Vehicle TheftArsonAlcohol CrimesDUIDrunkenness
LiquorDrug CrimesTotal DrugTotal Drug SalesTotal Drug PossessionCannabis PossessionCannabis SalesHeroin or Cocaine PossessionHeroin or Cocaine SalesOther Drug PossessionOther Drug SalesSynthetic Narcotic PossessionSynthetic Narcotic SalesGrey Collar and Property CrimesForgeryFraudStolen PropertyFinancial CrimesEmbezzlementTotal GamblingOther GamblingBookmakingNumbers LotterySex or Family CrimesOffenses Against the Family and Children
Other Sex Offenses
ProstitutionRapeViolent CrimesAggravated AssaultMurderNegligent ManslaughterRobberyWeapon Offenses
Other CrimesCurfewDisorderly ConductOther Non-trafficSuspicion
VandalismVagrancy
Simple
This data set has every crime and only the arrest categories that I created (see above).
If you have any questions, comments, or suggestions please contact me at jkkaplan6@gmail.com.

This document explain how data were generated and how to interpret them.

LICENSE: CC0 But if you want to combine data with other datasets, feel free to use them as if they were published under CC0 license.
Data were published in February 2017. At that time, Zenodo only provided CC BY, CC BY-SA, CC BY-NC, CC BY-ND and CC BY-NC-ND. No CC0 option was available.

HOW DATA WERE COLLECTED The 21 recorded sessions took place between February 2013 and December 2016.
Data were collected using Turning Technologies' remote controls (called clickers) and TurningPoint software.

The 4 versions of the quiz used during these 4 years are provided in the 'quizzes' folder for information purpose (in PDF and Powerpoint formats).

Turning Technologies records data in a closed format (.tpzx) that can be exported and converted them into 3 formats provided here (these 3 files contain the same data):

• Excel (.xslx)
• Comma-spearated values (.csv)
• SQLite (.sqlite)

The first one was directly exported from TurningPoint and is provided for Excel users who can't read CSV correctly.
CSV was converted from Excel and is provided for non-Excel users.
Finally, SQLite is provided in order to apply different sorting and filters to the data. It can be read using SQLite manager for Firefox (https://addons.mozilla.org/en-US/firefox/addon/sqlite-manager/).

CODEBOOK Here is the name, the meaning and the possible values of the columns (name - meaning [possible values]). If students didn't answer the question, the value is '-'.

Session - session number (chronological) [1 to 21] AcademicYear - academic year [12-13, 13-14, 14-15, 15-16, 16-17] Year - calendar year [2013, 2014, 2015, 2016] Month - month (number) [1 to 12] Day - day (number) [1 to 31] Section - section abbreviation [CH, ESC, GM, IF, SIE, SV] Level - students' level [BA2, BA3, MA] Language - course's language [FR or EN] DeviceID - clicker's ID [(unique ID within a session)] Q1 - answers to question 1 [A, B, C, D, E] Q2 - answers to question 2 [A, B, C, D] Q3 - answers to question 3 [A or B] Q4 - answers to question 4 [A or B] Q5 - answers to question 5 [A or B] Q6 - answers to question 6 [A or B] Q7 - answers to question 7 [A or B] Q8 - answers to question 8 [A or B] Q9 - answers to question 9 [A or B] Q8-9 - answers to the question 8-9 (merge) [A or B] Q10 - answers to question 10 [1, 2] Q11 - answers to question 11 [A or B] Q12 - answers to question 12 [A, B]

Section abbreviation meaning * CH: chemistry * ESC: school of criminal justice (Unil) * GM: mechanical engineering * IF: financial engineering * SIE: environmental engineering * SV: life sciences

Level meaning
* BA2: 2nd year of Bachelor * BA3: 3rd year of Bachelor * MA: Master level

Question types For some questions, multiple answers were allowed: Q1, Q2, Q10 & Q12.
Half of the questions have only one correct answer, true or false: Q3, Q5, Q6, Q7, Q8, Q9 & Q8-9.
Finally, for 2 questions only one answer was accepted, but there is not only one correct answer: Q4 & Q11.

INFORMATION ABOUT THE SESSIONS Except otherwise stated below, all sessions were conducted like the original one: Q1 to Q12 (no Q8-9). The original French version of the quiz has been translated into English for a few sessions with Master students. For sessions 14 and 20, Q5 was removed and Q8 & Q9 were merged in Q8-9.
Session 18 was a short one with only 7 sevens questions: Q1, Q2, Q3, Q4, Q6, Q7 & Q9.

CONTACT INFORMATION If you have any question about these data, contact formations.bib@epfl.ch.

IntroductionBreast cancer continues to be the most common malignancy and the leading cause of cancer-related deaths in Ethiopia. The poor prognosis and high mortality rate of breast cancer patients in the country are largely caused by late-stage diagnosis. Hence, understanding the epidemiology of late-stage diagnosis is essential to address this important problem. However, previous reports in Ethiopia indicated inconsistent findings. Therefore, this literature review was conducted to generate dependable evidence by summarizing the prevalence and determinants of late-stage diagnosis among breast cancer patients in Ethiopia.MethodsPertinent articles were retrieved by systematically searching on major electronic databases and gray literature. Data were extracted into an Excel spreadsheet and analyzed using the STATA 17 statistical software. The pooled estimates were summarized using the random effect meta-analysis model. Heterogeneity and small study effect were evaluated using the I2 statistics and Egger’s regression test in conjunction with the funnel plot, respectively. Meta-regression, sub-group analysis, and sensitivity analysis were also employed. Protocol registration number: CRD42024496237.ResultsThe pooled prevalence of late-stage diagnosis after combining reports of 24 studies with 8,677 participants was 65.85 (95% CI: 58.38, 73.32). Residence (adjusted OR: 1.92; 95% CI: 1.45, 2.53), patient delay at their first presentation (adjusted OR: 2.65; 95% CI: 1.56, 4.49), traditional medicine use (adjusted OR: 2.54; 95% CI: 1.89, 3.41), and breast self-examination practice (adjusted OR: 0.28; 95% CI: 0.09, 0.88) were significant determinants of late-stage diagnosis.ConclusionTwo-thirds of breast cancer patients in Ethiopia were diagnosed at an advanced stage. Residence, delay in the first presentation, traditional medicine use, and breast self-examination practice were significantly associated with late-stage diagnosis. Public education about breast cancer and its early detection techniques is crucial to reduce mortality and improve the survival of patients. Besides, improving access to cancer screening services is useful to tackle the disease at its curable stages.

Excel spreadsheet containing, in separate sheets, underlying numerical data used to generate the indicated figure panels.

This data is sourced from the Census 2011 and shows the population and population density by council area. Raw data sourced from http://www.scotlandscensus.gov.uk/en/censusresults/downloadablefiles.html and then manipulated in excel to merge a number of tables. The resulting data was joined to a shapefile of Scottish Council areas from sharegeo (http://www.sharegeo.ac.uk/handle/10672/305). Both sources should be attributed as the sources of the base data. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2012-12-19 and migrated to Edinburgh DataShare on 2017-02-21.

Excel spreadsheet containing the underlying numerical data for Figs 1C, 2C, 2D, 4B, 4C, 5A, 5B, S11, S12 and S14.