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.

For a comprehensive guide to this data and other UCR data, please see my book at ucrbook.comVersion 13 release notes:Changes R files from .rda to .rds.Fixes bug where the number_of_months_reported variable incorrectly was the largest of the number of months reported for a specific crime variable. For example, if theft was reported Jan-June and robbery was reported July-December in an agency, in total there were 12 months reported. But since each crime (and let's assume no other crime was reported more than 6 months of the year) only was reported 6 months, the number_of_months_reported variable was incorrectly set at 6 months. Now it is the total number of months reported of any crime. So it would be set to 12 months in this example. Thank you to Nick Eubank for alerting me to this issue.Adds rows even when a agency reported zero arrests that month; all arrest values are set to zero for these rows.Version 12 release notes:Adds 2019 data.Version 11 release notes:Changes release notes description, does not change data.Version 10 release notes:The data now has the following age categories (which were previously aggregated into larger groups to reduce file size): under 10, 10-12, 13-14, 40-44, 45-49, 50-54, 55-59, 60-64, over 64. These categories are available for female, male, and total (female+male) arrests. The previous aggregated categories (under 15, 40-49, and over 49 have been removed from the data). Version 9 release notes:For each offense, adds a variable indicating the number of months that offense was reported - these variables are labeled as "num_months_[crime]" where [crime] is the offense name. These variables are generated by the number of times one or more arrests were reported per month for that crime. For example, if there was at least one arrest for assault in January, February, March, and August (and no other months), there would be four months reported for assault. Please note that this does not differentiate between an agency not reporting that month and actually having zero arrests. The variable "number_of_months_reported" is still in the data and is the number of months that any offense was reported. So if any agency reports murder arrests every month but no other crimes, the murder number of months variable and the "number_of_months_reported" variable will both be 12 while every other offense number of month variable will be 0. Adds data for 2017 and 2018.Version 8 release notes:Adds annual data in R format.Changes project name to avoid confusing this data for the ones done by NACJD.Fixes bug where bookmaking was excluded as an arrest category. Changed the number of categories to include more offenses per category to have fewer total files. Added a "total_race" file for each category - this file has total arrests by race for each crime and a breakdown of juvenile/adult by race. Version 7 release notes: Adds 1974-1979 dataAdds monthly data (only totals by sex and race, not by age-categories). All data now from FBI, not NACJD. Changes some column names so all columns are <=32 characters to be usable in Stata.Changes how number of months reported is calculated. Now it is the number of unique months with arrest data reported - months of data from the monthly header file (i.e. juvenile disposition data) are not considered in this calculation. Version 6 release notes: Fix bug where juvenile female columns had the same value as juvenile male columns.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

Reddit: /r/technology (Submissions & Comments)

Title, Score, ID, URL, Comment Number, and Timestamp

By Reddit [source]

About this dataset

This dataset, labeled as Reddit Technology Data, provides thorough insights into the conversations and interactions around technology-related topics shared on Reddit – a well-known Internet discussion forum. This dataset contains titles of discussions, scores as contributed by users on Reddit, the unique IDs attributed to different discussions, URLs of those hidden discussions (if any), comment counts in each discussion thread and timestamps of when those conversations were initiated. As such, this data is supremely valuable for tech-savvy people wanting to stay up to date with the new developments in their field or professionals looking to keep abreast with industry trends. In short, it is a repository which helps people make sense and draw meaning out of what’s happening in the technology world at large - inspiring action on their part or simply educating them about forthcoming changes

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How to use the dataset

The dataset includes six columns containing title, score, url address link to the discussion page on Reddit itself ,comment count ,created time stamp meaning when was it posted/uploaded/communicated and body containing actual text written regarding that particular post/discussion. By separately analyzing each column it can be made out what type information user require in regard with various aspects related to technology based discussions. One can develop hypothesis about correlations between different factors associated with rating or comment count by separate analysis within those columns themselves like discuss what does people comment or react mostly upon viewing which type of post inside reddit ? Does high rating always come along with extremely long comments.? And many more .By researching this way one can discover real facts hidden behind social networking websites such as reddit which contains large amount of rich information regarding user’s interest in different topics related to tech gadgets or otherwise .We can analyze different trends using voice search technology etc in order visualize users overall reaction towards any kind of information shared through public forums like stack overflow sites ,facebook posts etc .These small instances will allow us gain heavy insights for research purpose thereby providing another layer for potential business opportunities one may benefit from over a given period if not periodcally monitored .

Research Ideas

• Companies can use this dataset to create targeted online marketing campaigns directed towards Reddit users interested in specific areas of technology.
• Academic researchers can use the data to track and analyze trends in conversations related to technology on Reddit over time.
• Technology professionals can utilize the comments and discussions on this dataset as a way of gauging public opinion and consumer sentiment towards certain technological advancements or products

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: technology.csv | Column name | Description | |:--------------|:--------------------------------------------------------------------------| | title | The title of the discussion. (String) | | score | The score of the discussion as measured by Reddit contributors. (Integer) | | url | The website URL associated with the discussion. (String) | | comms_num | The number of comments associated with the discussion. (Integer) | | created | The date and time the discussion was created. (DateTime) | | body | The body content of the discussion. (String) | | timestamp | The timestamp of the discussion. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Reddit.

This is the carbon monoxide data. Each sheet (tab) is formatted to be exported as a .csv for use with the R-code (AQ-June20.R). In order for this code to work properly, it is important that this file remain intact. Do not change the column names or codes for data, for example. And to be safe, don’t even sort. Just in case. One simple change in the excel file could make the code full of bugs.

For a comprehensive guide to this data and other UCR data, please see my book at ucrbook.comVersion 12 release notes:Adds 2019 data.Version 11 release notes:Changes release notes description, does not change data.Version 10 release notes:The data now has the following age categories (which were previously aggregated into larger groups to reduce file size): under 10, 10-12, 13-14, 40-44, 45-49, 50-54, 55-59, 60-64, over 64. These categories are available for female, male, and total (female+male) arrests. The previous aggregated categories (under 15, 40-49, and over 49 have been removed from the data). Version 9 release notes:For each offense, adds a variable indicating the number of months that offense was reported - these variables are labeled as "num_months_[crime]" where [crime] is the offense name. These variables are generated by the number of times one or more arrests were reported per month for that crime. For example, if there was at least one arrest for assault in January, February, March, and August (and no other months), there would be four months reported for assault. Please note that this does not differentiate between an agency not reporting that month and actually having zero arrests. The variable "number_of_months_reported" is still in the data and is the number of months that any offense was reported. So if any agency reports murder arrests every month but no other crimes, the murder number of months variable and the "number_of_months_reported" variable will both be 12 while every other offense number of month variable will be 0. Adds data for 2017 and 2018.Version 8 release notes:Adds annual data in R format.Changes project name to avoid confusing this data for the ones done by NACJD.Fixes bug where bookmaking was excluded as an arrest category. Changed the number of categories to include more offenses per category to have fewer total files. Added a "total_race" file for each category - this file has total arrests by race for each crime and a breakdown of juvenile/adult by race. Version 7 release notes: Adds 1974-1979 dataAdds monthly data (only totals by sex and race, not by age-categories). All data now from FBI, not NACJD. Changes some column names so all columns are <=32 characters to be usable in Stata.Changes how number of months reported is calculated. Now it is the number of unique months with arrest data reported - months of data from the monthly header file (i.e. juvenile disposition data) are not considered in this calculation. Version 6 release notes: Fix bug where juvenile female columns had the same value as juvenile male columns.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 (ASR) 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 1974-2019 into a single file for each group of crimes. Each monthly file is only a single year as my laptop can't handle combining all the years together. These files are quite large and may take some time to load. Col

This is the raw H2S data- concentration of H2S in parts per million in the biogas. Each sheet (tab) is formatted to be exported as a .csv for use with the R-code (AQ-June20.R). In order for this code to work properly, it is important that this file remain intact. Do not change the column names or codes for data, for example. And to be safe, don’t even sort. One simple change in the excel file could make the code full of bugs.

This is the gravimetric data used to calibrate the real time readings. Each sheet (tab) is formatted to be exported as a .csv for use with the R-code (AQ-June20.R). In order for this code to work properly, it is important that this file remain intact. Do not change the column names or codes for data, for example. And to be safe, don’t even sort. One simple change in the excel file could make the code full of bugs.

File List Ecol_Monograph_supplement_code_biomod2.txt (md5: 1468e75dbf74ed624a8dce871743f924) Ecol_Monograph_supplement_code_dismo_1.txt (md555b20fbe747f7601c53d5b56a93459ea: ) Ecol_Monograph_supplement_code_dismo_2.txt (md5: a33a1745062f1bf816c3d9ec797cdd46) Ecol_Monograph_supplement_code_dismo_3.txt (md5: aff301c5ba52f04eff85e561122964c4) Ecol_Monograph_supplement_code_dismo_4.txt (md5: 244ff730dbd9da02a5439cfd95a439ca) Ecol_Monograph_supplement_code_dismo_5.txt (md5: bec6a05bf1d737b941d0a7a00bde3658) lot_line_section_with_predictors.csv (md5: 48dc1b92e2d3d3b3e4875ef0dc3b87a7) township_bt_post_with_predictors.csv (md5: 86f08554a0a65fec8065f85335aa8ec5) township_line_section_with_predictors.csv (md5: d028af68dcd8f7bca5b28e969cc5c796) biomod2_predictors.zip (md5: 7ab5a1d2ef1847fe64a47483e8220d70)

  Description

   This supplement contains the data and code that were used to train and evaluate species distribution models (SDMs). Included are six (6) .txt files that contain code to be run in R, and three (3) .csv files that contain the training data and evaluation data. For all files that contain code, comments are included (“#...”) to describe its functioning.

    There are two notes regarding the code files in this supplement. First, users seeking to recreate the results should be aware that minor edits to the code are necessary, in order to make sure all pathnames that are referenced in the code will match the locations where the user is storing the data files. Second, the presented code is for training SDMs that include Native American variables (NAVs). A few minor edits to the code would need to be made, in order to run SDMs that exclude NAVs; these edits are documented in the comments of the code files. Both edits are minor and should take little time to make.

    Also worth noting is the considerable processing time required to train and evaluate the models. While the “biomod2” code is highly-automated, it could still require several hours to a few days to run, on a personal computer. The “dismo” codes could take several days to one week to run properly; these codes also involve much more “manual” inputting of blocks of code into R. Alternatively, more advanced users of R could edit the code to function as a script and/or be more automated.

   The following is a description of each individual file.


     Ecol_Monograph_supplement_code_biomod2.txt – this file contains the code for training SDMs from the Holland Land Company (HLC) line-description (or “line section”) data, using three SDM algorithms from the “biomod2” package in R: Generalized Additive Models (GAMs), Generalized Linear Models (GLMs), and Multivariate Adaptive Regression Splines (MARS).
     Five .txt files contain additional code for training and evaluating boosted regression tree (BRT) models, using the “dismo” package in R. The code for BRT model development was broken down into five files, which must be run in succession. Note that due to the “stochastic” nature of BRT models, slightly different model results may result, in comparison to the results reported in the article.
     Ecol_Monograph_supplement_code_dismo_1.txt – this code loads the training data, and trains an initial set of BRT models. 
     Ecol_Monograph_supplement_code_dismo_2.txt – this code runs a procedure that suggests the number of variables that can be dropped from the initial set of BRT models.
     Ecol_Monograph_supplement_code_dismo_3.txt – this code creates a set of simplified BRT models with fewer variables, as determined by the previous step.
     Ecol_Monograph_supplement_code_dismo_4.txt – this code loads evaluation data, loads raster versions of predictor variables, projects models into geographic space, calculates variable importance, plots response curves, and evaluates models upon training data and evaluation data.
     Ecol_Monograph_supplement_code_dismo_5.txt – this code saves false positive rates and false negative rates for each model, when evaluated upon the training data and evaluation data.
    .csv files – these files contain the training data and evaluation data:
     lot_line_section_with_predictors.csv – this file contains the line-description data that was used to train SDMs.
     township_bt_post_with_predictors.csv – this file contains the township bearing-tree data, which was used to evaluate SDMs.
     township_line_section_with_predictors.csv – this file contains the township line-description data, which was used to evaluate SDMs.
     The township data above were used with the permission of Dr. Yi-Chen Wang. For more information regarding these datasets, see:

      Wang, Y.-C. 2007. Spatial patterns and vegetation-site relationships of the presettlement forests in western New York, USA. Journal of Biogeography 34:500–513.
      Tulowiecki, S. J., C. P. S. Larsen, and Y.-C. Wang. 2014. Effects of positional error on modeling species distributions: a perspective using presettlement land survey records. Plant Ecology 216:67–85. 


   The following table contains descriptions of the columns, and checksum values, for the .csv files (sorted alphabetically by column name). With the exception of the “weights” columns, the three .csv files share the same column names (but obviously with different values). The evaluation data (“township_bt_post_with_ predictors.csv” and “township_line_section_with_predictors.csv”) do not contain case weight columns, because case weights were only used when training models using the training data (“lot_line_section_with_ predictors.csv”). There are no blank cell values in these .csv files.
    -- TABLE: Please see in attached file. --

    biomod2_predictors.zip – this zipped file contains the predictor variables in raster format (coordinate system: UTM Zone 17N) that were used to project SDMs into geographic space, in order to train SDMs and create prediction surfaces.

Reddit: /r/worldnews (Submissions & Comments)

Analyzing Post Engagement

By Reddit [source]

About this dataset

This dataset offers insight into the ways that public opinion shapes the world news cycle. Gathering posts from various topics such as politics, current affairs, socio-economic issues, sports, entertainment and more from the subredditworldnews subreddit, this dataset provides engagement data from each post in order to analyze public sentiment. With columns including title, score, url, comms_num, created timestamp and body text for each post in the collection it is easy to assess discussion thread topics or dig deep into individual posts to ascertain what perspectives on world news have the most traction. From questions of foreign policy to environmental action and social movements it is possible with this tool analyse how these stories shape our global outlook

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Research Ideas

• Looking at correlations between post engagement and the topics of the posts to better understand the most popular topics on world news.
• Analyzing the differences in post engagement according to geographic regions to better understand what is trending in certain areas of the world.
• Tracking changes in engagement over time as a way to assess public opinion about specific news cycles or events

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: worldnews.csv | Column name | Description | |:--------------|:--------------------------------------------------------| | title | The title of the post. (String) | | score | The number of upvotes the post has received. (Integer) | | url | The URL of the post. (String) | | comms_num | The number of comments the post has received. (Integer) | | created | The date and time the post was created. (Datetime) | | body | The body of the post. (String) | | timestamp | The date and time the post was last updated. (Datetime) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Reddit.

Methane concentration of biogas. Each sheet (tab) is formatted to be exported as a .csv for use with the R-code (AQ-June20.R). In order for this code to work properly, it is important that this file remain intact. Do not change the column names or codes for data, for example. And to be safe, don’t even sort. Just in case. One simple change in the excel file could make the code full of bugs.

Reddit: /r/Art

Examining Content by Title, Score, ID, URL, Comments, Create Date, and Timestamp

By Reddit [source]

About this dataset

This dataset offers an in-depth exploration of the artistic world of Reddit, with a focus on the posts available on the website. By examining the titles, scores, ID's, URLs, comments, creation dates and timestamps associated with each post about art on Reddit, researchers can gain invaluable insight into how art enthusiasts share their work and build networks within this platform. Through analyzing this data we can understand what sorts of topics attract more attention from viewers and how members interact with one another in online discussions. Moreover, this dataset has potential to explore some of the larger underlying issues that shape art communities today - from examining production trends to better understanding consumption patterns. Overall, this comprehensive dataset is an essential resource for those aiming to analyze and comprehend digital spaces where art is circulated and discussed - giving unique insight into how ideas are created and promoted throughout creative networks

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How to use the dataset

This dataset is an excellent source of information related to online art trends, providing comprehensive analysis of Reddit posts related to art. In this guide, we’ll discuss how you can use this dataset to gather valuable insights about the way in which art is produced and shared on the web.
First and foremost, you should start by familiarizing yourself with the columns included in the dataset. Each post contains a title, score (number of upvotes), URL, comments (number of comments), created date and timestamp. When interpreting each column individually or comparing different posts/threads, these values will provide invaluable insight into topics such as most discussed or favored content within the Reddit community.
After exploring the general features within each post/thread in your analysis it’s time to move onto more specific components such as body content (including images) and creative dates - when users began responding and interacting with content posted about a specific topic or action related item). Utilizing these variables will help researchers uncover meaningful patterns regarding how communities interact with certain types of content over longer periods of time & also give context from what type of topics are trending at any given moment when analyzing at shorter intervals.
Finally one last creative output that can stem from using this data set revolves around examining titles for common words & phrases that appear often among posts discussing similar types of artwork or other forms media production - identifying potential keywords & symbols associated across several different groups can paint a holistic picture regards what kind engagement each group desires while they engage amongst other like-minded individuals further aided by parameters presented through number scores what helps measure overall reception per submissions or individual thoughts presented in comment thread discussions among others known similar outlets available on site itself! Here's hoping utilizing these techniques may bring attention to some possible conclusions derived already exists previously undiscovered apart our eyes – good luck everyone!

Research Ideas

• Analyzing topics and themes within art posts to determine what content is most popular.
• Examining the score of art posts to determine how the responding audience engages with each piece.
• Comparing across different subreddits to explore the ‘meta-discourse’ of topics that appear in multiple forums or platforms

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: Art.csv | Column name | Description | |:--------------|:--------------------------------------------------------| | title | The title of the post. (String) | | score | The number of upvotes the post has received. (Integer) | | url | The URL of the post. (String) | | comms_num | ...

Reddit: /r/news

Exploring Topics, Scores, and Engagement

By Reddit [source]

About this dataset

This dataset provides an in-depth look into learning what communities find important and engaging in the news. With this data, researchers can discover trends related to user engagement and popular topics within subreddits. By examining the “score” and “comms_num” columns, our researchers will be able to pinpoint which topics are most liked, discussed or shared within the various subreddits. Researchers may also gain insights into not only how popular a topic is but how it is growing over time. Additionally, by exploring the body column of our dataset, researchers can understand more about which types of news stories drive conversation within particular subreddits—providing an opportunity for deeper analysis of that subreddit’s diverse community dynamics

The dataset includes eight columns: title, score, id, url, comms_num created**body and timestamp** which can help us identify key insights into user engagement among popular subreddits. With this data we may also determine relationships between topics of discussion and their impact on user engagement allowing us to create a better understanding surrounding issue-based conversations online as well as uncover emerging trends in online news consumption habits

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How to use the dataset

This dataset is useful for those who are looking to gain insight into the popularity and user engagement of specific subreddits. The data includes 8 different columns including title, score, id, url, comms_num, created, body and timestamp. This can provide valuable information about how users view and interact with particular topics across various subreddits.

In this guide we’ll look at how you can use this dataset to uncover trends in user engagement on topics within specific subreddits as well as measure the overall popularity of these topics within a subreddit.

1) Analyzing Score: By analyzing the “score” column you can determine which news stories are popular in a particular subreddit and which ones aren't by looking at how many upvotes each story has received. With this data you will be able to determine trends in what types of stories users preferred within a particular subreddit over time.

2) Analyzing Comms_Num: Similarly to analyzing the score column you can analyze the “comms_num” column to see which news stories had more engagement from users by tracking number of comments received on each post. Knowing these points can provide insight into what types of stories tend to draw more comment activity from users in certain subreddits from one day or an extended period of time such tracking post activity for multiple weeks or months at once 3) Analyzing Body: Additionally by looking at the “body” column for each post researchers can gain a better understanding which kinds of topics/news draw attention among specific Reddit communities.. With that complete picture researchers have access not only to data measuring Reddit buzz but also access topic discussion/comments helping generate further insights into why certain posts might be popular or receive more comments than others

Overallthis dataset provides valuable insights about user engagedment related specifically topics trending accross subsbreddits allowing anyone interested reseraching such things easier way access those insights all one place

Research Ideas

• Grouping news topics within particular subreddits and assessing the overall popularity of those topics in terms of scores/user engagement.
• Correlating user engagement with certain news topics to understand how they influence discussion or reactions on a subreddit.
• Examining the potential correlation between score and the actual body content of a given post to assess what types of content are most successful in gaining interest from users and creating positive engagement for posts

Acknowledgements

If you use this dataset in your research, please credit the original authors.

Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: news.csv | Column name | Description ...

The Meta-Corpus of Datasets: The Reddit Dataset

The Complete Collection of Datasets Posted on Reddit

By SocialGrep [source]

About this dataset

A subreddit dataset is a collection of posts and comments made on Reddit's /r/datasets board. This dataset contains all the posts and comments made on the /r/datasets subreddit from its inception to March 1, 2022. The dataset was procured using SocialGrep. The data does not include usernames to preserve users' anonymity and to prevent targeted harassment

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How to use the dataset

In order to use this dataset, you will need to have a text editor such as Microsoft Word or LibreOffice installed on your computer. You will also need a web browser such as Google Chrome or Mozilla Firefox.

Once you have the necessary software installed, open the The Reddit Dataset folder and double-click on the the-reddit-dataset-dataset-posts.csv file to open it in your preferred text editor.

In the document, you will see a list of posts with the following information for each one: title, sentiment, score, URL, created UTC, permalink, subreddit NSFW status, and subreddit name.

You can use this information to analyze trends in data sets posted on /r/datasets over time. For example, you could calculate the average score for all posts and compare it to the average score for posts in specific subReddits. Additionally, sentiment analysis could be performed on the titles of posts to see if there is a correlation between positive/negative sentiment and upvotes/downvotes

Research Ideas

• Finding correlations between different types of datasets
• Determining which datasets are most popular on Reddit
• Analyzing the sentiments of post and comments on Reddit's /r/datasets board

Acknowledgements

If you use this dataset in your research, please credit the original authors.

Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: the-reddit-dataset-dataset-comments.csv | Column name | Description | |:-------------------|:---------------------------------------------------| | type | The type of post. (String) | | subreddit.name | The name of the subreddit. (String) | | subreddit.nsfw | Whether or not the subreddit is NSFW. (Boolean) | | created_utc | The time the post was created, in UTC. (Timestamp) | | permalink | The permalink for the post. (String) | | body | The body of the post. (String) | | sentiment | The sentiment of the post. (String) | | score | The score of the post. (Integer) |

File: the-reddit-dataset-dataset-posts.csv | Column name | Description | |:-------------------|:---------------------------------------------------| | type | The type of post. (String) | | subreddit.name | The name of the subreddit. (String) | | subreddit.nsfw | Whether or not the subreddit is NSFW. (Boolean) | | created_utc | The time the post was created, in UTC. (Timestamp) | | permalink | The permalink for the post. (String) | | score | The score of the post. (Integer) | | domain | The domain of the post. (String) | | url | The URL of the post. (String) | | selftext | The self-text of the post. (String) | | title | The title of the post. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit SocialGrep.

For a comprehensive guide to this data and other UCR data, please see my book at ucrbook.comVersion 15 release notes:Adds 2022-2024 data.Converts data from wide to long to be more consistent with the raw data.Adds parquet file typeVersion 15 release notes:Adds 2021 data.Version 14 release notes:Adds 2020 data. Please note that the FBI has retired UCR data ending in 2020 data so this will be the last Arrests by Age, Sex, and Race data they release. Version 13 release notes:Changes R files from .rda to .rds.Fixes bug where the number_of_months_reported variable incorrectly was the largest of the number of months reported for a specific crime variable. For example, if theft was reported Jan-June and robbery was reported July-December in an agency, in total there were 12 months reported. But since each crime (and let's assume no other crime was reported more than 6 months of the year) only was reported 6 months, the number_of_months_reported variable was incorrectly set at 6 months. Now it is the total number of months reported of any crime. So it would be set to 12 months in this example. Thank you to Nick Eubank for alerting me to this issue.Adds rows even when a agency reported zero arrests that month; all arrest values are set to zero for these rows.Version 12 release notes:Adds 2019 data.Version 11 release notes:Changes release notes description, does not change data.Version 10 release notes:The data now has the following age categories (which were previously aggregated into larger groups to reduce file size): under 10, 10-12, 13-14, 40-44, 45-49, 50-54, 55-59, 60-64, over 64. These categories are available for female, male, and total (female+male) arrests. The previous aggregated categories (under 15, 40-49, and over 49 have been removed from the data). Version 9 release notes:For each offense, adds a variable indicating the number of months that offense was reported - these variables are labeled as "num_months_[crime]" where [crime] is the offense name. These variables are generated by the number of times one or more arrests were reported per month for that crime. For example, if there was at least one arrest for assault in January, February, March, and August (and no other months), there would be four months reported for assault. Please note that this does not differentiate between an agency not reporting that month and actually having zero arrests. The variable "number_of_months_reported" is still in the data and is the number of months that any offense was reported. So if any agency reports murder arrests every month but no other crimes, the murder number of months variable and the "number_of_months_reported" variable will both be 12 while every other offense number of month variable will be 0. Adds data for 2017 and 2018.Version 8 release notes:Adds annual data in R format.Changes project name to avoid confusing this data for the ones done by NACJD.Fixes bug where bookmaking was excluded as an arrest category. Changed the number of categories to include more offenses per category to have fewer total files. Added a "total_race" file for each category - this file has total arrests by race for each crime and a breakdown of juvenile/adult by race. Version 7 release notes: Adds 1974-1979 dataAdds monthly data (only totals by sex and race, not by age-categories). All data now from FBI, not NACJD. Changes some column names so all columns are <=32 characters to be usable in Stata.Changes how number of months reported is calculated. Now it is the number of unique months with arrest data reported - months of data from the monthly header file (i.e. juvenile disposition data) are not considered in this calculation. Version 6 release notes: Fix bug where juvenile female columns had the same value as juvenile male columns.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 wi

Reddit: /r/Damnthatsinteresting

Investigating Popularity, Score and Engagement Across Subreddits

By Reddit [source]

About this dataset

This dataset provides valuable insights into user engagement and popularity across the subreddit Damnthatsinteresting. With detailed metrics on various discussions such as the title, score, id, URL, comments, created date and time, body and timestamp of each discussion. This dataset opens a window into the world of user interaction on Reddit by letting researchers align their questions with data-driven results to understand social media behavior. Gain an understanding of what drives people to engage in certain conversations as well as why certain topics become trending phenomena – it’s all here for analysis. Enjoy exploring this fascinating collection of information about Reddit users' activities!

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How to use the dataset

This dataset provides valuable insights into user engagement and the impact of users interactions on the popular subreddit DamnThatsInteresting. Exploring this dataset can help uncover trends in participation, what content is resonating with viewers, and how different users are engaging with each other. In order to get the most out of this dataset, you will need to understand its structure in order to explore and extract meaningful insights. The columns provided include: title, score, url, comms_num, created date/time (created), body and timestamp.

Research Ideas

• Analyzing the impact of user comments on the popularity and engagement of discussions
• Examining trends in user behavior over time to gain insight into popular topics of discussion
• Investigating which discussions reach higher levels of score, popularity or engagement to identify successful strategies for engaging users

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: Damnthatsinteresting.csv | Column name | Description | |:--------------|:-----------------------------------------------------------------------------------------------------------| | title | The title of the discussion thread. (String) | | score | The number of upvotes the discussion has received from users. (Integer) | | url | The URL link for the discussion thread itself. (String) | | comms_num | The number of comments made on a particular discussion. (Integer) | | created | The date and time when the discussion was first created on Reddit by its original poster (OP). (DateTime) | | body | Full content including text body with rich media embedded within posts such as images/videos etc. (String) | | timestamp | When was last post updated by any particular user. (DateTime) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Reddit.

For a comprehensive guide to this data and other UCR data, please see my book at ucrbook.comVersion 14 release notes:Adds 2020 data. Please note that the FBI has retired UCR data ending in 2020 data so this will be the last Arrests by Age, Sex, and Race data they release. Version 13 release notes:Changes R files from .rda to .rds.Fixes bug where the number_of_months_reported variable incorrectly was the largest of the number of months reported for a specific crime variable. For example, if theft was reported Jan-June and robbery was reported July-December in an agency, in total there were 12 months reported. But since each crime (and let's assume no other crime was reported more than 6 months of the year) only was reported 6 months, the number_of_months_reported variable was incorrectly set at 6 months. Now it is the total number of months reported of any crime. So it would be set to 12 months in this example. Thank you to Nick Eubank for alerting me to this issue.Adds rows even when a agency reported zero arrests that month; all arrest values are set to zero for these rows.Version 12 release notes:Adds 2019 data.Version 11 release notes:Changes release notes description, does not change data.Version 10 release notes:The data now has the following age categories (which were previously aggregated into larger groups to reduce file size): under 10, 10-12, 13-14, 40-44, 45-49, 50-54, 55-59, 60-64, over 64. These categories are available for female, male, and total (female+male) arrests. The previous aggregated categories (under 15, 40-49, and over 49 have been removed from the data). Version 9 release notes:For each offense, adds a variable indicating the number of months that offense was reported - these variables are labeled as "num_months_[crime]" where [crime] is the offense name. These variables are generated by the number of times one or more arrests were reported per month for that crime. For example, if there was at least one arrest for assault in January, February, March, and August (and no other months), there would be four months reported for assault. Please note that this does not differentiate between an agency not reporting that month and actually having zero arrests. The variable "number_of_months_reported" is still in the data and is the number of months that any offense was reported. So if any agency reports murder arrests every month but no other crimes, the murder number of months variable and the "number_of_months_reported" variable will both be 12 while every other offense number of month variable will be 0. Adds data for 2017 and 2018.Version 8 release notes:Adds annual data in R format.Changes project name to avoid confusing this data for the ones done by NACJD.Fixes bug where bookmaking was excluded as an arrest category. Changed the number of categories to include more offenses per category to have fewer total files. Added a "total_race" file for each category - this file has total arrests by race for each crime and a breakdown of juvenile/adult by race. Version 7 release notes: Adds 1974-1979 dataAdds monthly data (only totals by sex and race, not by age-categories). All data now from FBI, not NACJD. Changes some column names so all columns are <=32 characters to be usable in Stata.Changes how number of months reported is calculated. Now it is the number of unique months with arrest data reported - months of data from the monthly header file (i.e. juvenile disposition data) are not considered in this calculation. Version 6 release notes: Fix bug where juvenile female columns had the same value as juvenile male columns.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_heroi

Version 10 release notes:Changes release notes description, does not change data.Version 9 release notes:Adds 2019 data.Version 8 release notes:Adds 2018 data.Changes source of data for years 1985-2018 to be directly from the FBI. 2018 data was received via email from the FBI, 2016-2017 is from the FBI who mailed me a DVD, and 1985-2015 data is from the FBI's Crime Data Explorer site (https://crime-data-explorer.fr.cloud.gov/downloads-and-docs).Adds .csv version of the data.Makes minor changes to value labels for consistency and to fix grammar. Version 7 release notes:Changes project name to avoid confusing this data for the ones done by NACJD.Version 6 release notes:Adds 2017 data.Version 5 release notes:Adds 2016 data.Standardizes the "group" column which categorizes cities and counties by population.Arrange rows in descending order by year and ascending order by ORI. Version 4 release notes: Fix bug where Philadelphia Police Department had incorrect FIPS county code. Version 3 Release Notes:Merges data with LEAIC data to add FIPS codes, census codes, agency type variables, and ORI9 variable.Change column names for relationship variables from offender_n_relation_to_victim_1 to victim_1_relation_to_offender_n to better indicate that all relationship are victim 1's relationship to each offender. Reorder columns.This is a single file containing all data from the Supplementary Homicide Reports from 1976 to 2018. The Supplementary Homicide Report provides detailed information about the victim, offender, and circumstances of the murder. Details include victim and offender age, sex, race, ethnicity (Hispanic/not Hispanic), the weapon used, circumstances of the incident, and the number of both offenders and victims. Years 1976-1984 were downloaded from NACJD, while more recent years are from the FBI. All files came as ASCII+SPSS Setup files and were cleaned using R. The "cleaning" just means that column names were standardized (different years have slightly different spellings for many columns). Standardization of column names is necessary to stack multiple years together. Categorical variables (e.g. state) were also standardized (i.e. fix spelling errors, have terminology be the same across years). The following is the summary of the Supplementary Homicide Report copied from ICPSR's 2015 page for the data.The Uniform Crime Reporting Program Data: Supplementary Homicide Reports (SHR) provide detailed information on criminal homicides reported to the police. These homicides consist of murders; non-negligent killings also called non-negligent manslaughter; and justifiable homicides. UCR Program contributors compile and submit their crime data by one of two means: either directly to the FBI or through their State UCR Programs. State UCR Programs frequently impose mandatory reporting requirements which have been effective in increasing both the number of reporting agencies as well as the number and accuracy of each participating agency's reports. Each agency may be identified by its numeric state code, alpha-numeric agency ("ORI") code, jurisdiction population, and population group. In addition, each homicide incident is identified by month of occurrence and situation type, allowing flexibility in creating aggregations and subsets.

Intro

This dataverse contains the data referenced in Rieth et al. (2017). Issues and Advances in Anomaly Detection Evaluation for Joint Human-Automated Systems. To be presented at Applied Human Factors and Ergonomics 2017.

Content

Each .RData file is an external representation of an R dataframe that can be read into an R environment with the 'load' function. The variables loaded are named ‘fault_free_training’, ‘fault_free_testing’, ‘faulty_testing’, and ‘faulty_training’, corresponding to the RData files.

Each dataframe contains 55 columns:

Column 1 ('faultNumber') ranges from 1 to 20 in the “Faulty” datasets and represents the fault type in the TEP. The “FaultFree” datasets only contain fault 0 (i.e. normal operating conditions).

Column 2 ('simulationRun') ranges from 1 to 500 and represents a different random number generator state from which a full TEP dataset was generated (Note: the actual seeds used to generate training and testing datasets were non-overlapping).

Column 3 ('sample') ranges either from 1 to 500 (“Training” datasets) or 1 to 960 (“Testing” datasets). The TEP variables (columns 4 to 55) were sampled every 3 minutes for a total duration of 25 hours and 48 hours respectively. Note that the faults were introduced 1 and 8 hours into the Faulty Training and Faulty Testing datasets, respectively.

Columns 4 to 55 contain the process variables; the column names retain the original variable names.

Acknowledgements

This work was sponsored by the Office of Naval Research, Human & Bioengineered Systems (ONR 341), program officer Dr. Jeffrey G. Morrison under contract N00014-15-C-5003. The views expressed are those of the authors and do not reflect the official policy or position of the Office of Naval Research, Department of Defense, or US Government.

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Reddit: /r/AdviceAnimals (Submissions & Comments)

Exploring Post and Comment Dynamics, Popular Content, and Community Contributors

By Reddit [source]

About this dataset

The AdviceAnimals.csv dataset provides users with an unprecedented look into the comment and score dynamics of one of Reddit's most iconic subreddits: Advice Animals. Not only does this data offer insights into post-specific score distributions and the kinds of conversations that ensue, but it also allows users to identify popular posts based on their upvote count, track meaningful conversations between prolific commenters, spot trends in the subreddit over time, and gain a clearer understanding of what content is valued most within this specific community. For each post in the subreddit, seven columns are included; title, score, id, URL, number of comments made on a post (comms_num), creation date/time (created), body text/media content holds or links to within a post (body) and timestamp. By analyzing this data at a granular level you can gain valuable insights into both Reddit at large as well as its influential part-subreddits like Advice Animals - helping aspiring moderators identify effective conversation dynamics for similar communities they may be managing!

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How to use the dataset

The dataset ‘Uncovering Community Dynamics of Reddit’s Advice Animals Subreddit’ can be used to uncover insights into the community dynamics of this particular Reddit subreddit. The dataset contains seven columns, including the post title, score, comment number, URL, creation date and time, body and timestamp. Here are some ways you can use this data to gain insight into the community dynamics:

Research Ideas

• Identifying the most prolific contributors in the subreddit by analyzing comment and score patterns over time.
• Examining post content to learn about trending topics in the subreddit.
• Monitoring score dynamics to identify popular posts and observe trends in different types of content that are liked by users of the subreddit

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: AdviceAnimals.csv | Column name | Description | |:--------------|:--------------------------------------------------------| | title | The title of the post. (String) | | score | The number of upvotes the post has received. (Integer) | | url | The URL of the post. (String) | | comms_num | The number of comments the post has received. (Integer) | | created | The date and time the post was created. (DateTime) | | body | The body text of the post. (String) | | timestamp | The timestamp of when the post was created. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Reddit.

Reddit: /r/videos

Insights on Popularity and Content Quality

By Reddit [source]

About this dataset

This dataset explores the media content on Reddit and how it is received by its community, providing detailed insights into both the popularity and quality of subreditvideos. Here you will find data about videos posted on Reddit, compiled from various metrics such as their upvotes, number of comments, date and time posted, body text and more. With this data you can dive deeper into the types of videos being shared and the topics being discussed – gaining a better understanding of what resonates with the Reddit community. This information allows us to gain insight into what kind of content has potential to reach a wide audience on Reddit; it also reveals which types of videos have been enjoying popularity amongst users over time. These insights can help researchers uncover valuable findings about media trends on popular social media sites such as Reddit – so don't hesitate to explore!

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How to use the dataset

How To Use This Dataset

This dataset is a great resource for analyzing the content and popularity of videos posted on Reddit. It provides various metrics such as score, url, comment count and creation date that let you compare the different types of content being shared on the subredditvideos subreddit.

To get started, take a look at the title field for each post. This gives you an idea of what type of video is being shared, which can be helpful in understanding what topics are popular on the platform.

Next, use the score field to identify posts that have done well in terms of receiving upvotes from users. The higher its score, the more popular it has been with viewers. A higher score does not necessarily indicate higher quality however; take a closer look at each post's body field to get an idea for its content quality before making assumptions about its value based solely off of its high score. Having said that, top scoring posts could be considered further when doing research analysis into popular topics or trends in media consumption behavior across Reddit’s userbase (e.g., trending topics among young adults). The url field provides you with links to directly access videos so you can review them yourself before sharing them or forwarding them onto friends or colleagues for their feedback/insight as well (something that could be done further depending on how detailed your research project requires). The comms_num column represents how many comments each video has received which may give insight into how engaged viewers have been when viewing stories submitted by this particular sub-reddit’s members - useful information if interactions/conversations surrounding particular types of content are part of your research objective too! Finally make sure to check out timestamp column as this records when each story was created - important information whenever attempting to draw conclusive insights from time-oriented data points (a time series analysis would serve very handy here!).
Knowing all these features listed above should give researchers an easily accessible source into exploring popularity and quality levels amongst Reddit’s shared media channels – uncovering potentially useful insights related specifically those moving image stories found within subredditvideos are made available via this dataset here!

Research Ideas

• Identifying and tracking trends in the popularity of different genres of videos posted on Reddit, such as interviews, music videos, or educational content.
• Investigating audience engagement with certain types of content to determine the types of posts that resonate most with users on Reddit.
• Examining correlations between video score or comment count and specific video characteristics such as length, topic or visual style

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: videos.csv | Column name | Description | |:--------------|:--------------------------------------------------------------------------| | title | The title of ...