Officer Involved Shooting (OIS) Database and Statistical Analysis. Data is updated after there is an officer involved shooting.PIU#Incident # - the number associated with either the incident or used as reference to store the items in our evidence rooms Date of Occurrence Month - month the incident occurred (Note the year is labeled on the tab of the spreadsheet)Date of Occurrence Day - day of the month the incident occurred (Note the year is labeled on the tab of the spreadsheet)Time of Occurrence - time the incident occurredAddress of incident - the location the incident occurredDivision - the LMPD division in which the incident actually occurredBeat - the LMPD beat in which the incident actually occurredInvestigation Type - the type of investigation (shooting or death)Case Status - status of the case (open or closed)Suspect Name - the name of the suspect involved in the incidentSuspect Race - the race of the suspect involved in the incident (W-White, B-Black)Suspect Sex - the gender of the suspect involved in the incidentSuspect Age - the age of the suspect involved in the incidentSuspect Ethnicity - the ethnicity of the suspect involved in the incident (H-Hispanic, N-Not Hispanic)Suspect Weapon - the type of weapon the suspect used in the incidentOfficer Name - the name of the officer involved in the incidentOfficer Race - the race of the officer involved in the incident (W-White, B-Black, A-Asian)Officer Sex - the gender of the officer involved in the incidentOfficer Age - the age of the officer involved in the incidentOfficer Ethnicity - the ethnicity of the suspect involved in the incident (H-Hispanic, N-Not Hispanic)Officer Years of Service - the number of years the officer has been serving at the time of the incidentLethal Y/N - whether or not the incident involved a death (Y-Yes, N-No, continued-pending)Narrative - a description of what was determined from the investigationContact:Carol Boylecarol.boyle@louisvilleky.gov

The total number of intersection crashes in Western Australia. The intersection contains the total number of aggregated crashes for all crashes recorded in the last 5 calendar years.Note: The 2024 records have been temporarily removed from the dataset. The crash data now covers the five-year period from 2019 to 2023. We apologise for any inconvenience.

Crashes are recorded in the Integrated Road Information System (IRIS). This layer shows the total number of crashes at each intersection and is provided for information only.

Note that you are accessing this data pursuant to a Creative Commons (Attribution) Licence which has a disclaimer of warranties and limitation of liability. You accept that the data provided pursuant to the Licence is subject to changes.

Pursuant to section 3 of the Licence you are provided with the following notice to be included when you Share the Licenced Material:- “The Commissioner of Main Roads is the creator and owner of the data and Licenced Material, which is accessed pursuant to a Creative Commons (Attribution) Licence, which has a disclaimer of warranties and limitation of liability.

Crash Data Dictionary

Creative Commons CC BY 4.0

Data for Figure SPM.8 from the Summary for Policymakers (SPM) of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

Figure SPM.8 shows selected indicators of global climate change under the five core scenarios used in this report.

How to cite this dataset

When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates:

IPCC, 2021: Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 3−32, doi:10.1017/9781009157896.001.

Figure subpanels

The figure has five panels, with data provided for all panels in subdirectories named panel_a, panel_b, panel_c, panel_d and panel_e.

List of data provided

This dataset contains:

• Historical, SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 Global Surface Air Temperature (GSAT) anomalies relative to 1850-1900 (20 year means)
• Historical, SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 September sea-ice area
• Historical, SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 Global ocean surface pH
• Historical sea level relative to 1900 from gauges (to 1992) and altimeters (1993 on) (offset 0.158 m vs. 1995-2014)
• AR6 sea level projections relative to 1900 (offset 0.158 m vs. 1995-2014)
• AR6 assessed global mean sea level at 2300 relative to 1900 (offset 0.158 m vs. 1995-2014)

The five illustrative SSP (Shared Socio-economic Pathway) scenarios are described in Box SPM.1 of the Summary for Policymakers and Section 1.6.1.1 of Chapter 1.

Data provided in relation to figure

Panel a: Near-Surface Air Temperature

• Data file: panel_a/tas_global_Historical.csv (black line and grey shading)
• Data file: panel_a/tas_global_SSP1_1_9.csv (cyan line)
• Data file: panel_a/tas_global_SSP1_2_6.csv (blue line and blue shading)
• Data file: panel_a/tas_global_SSP2_4_5.csv (orange line)
• Data file: panel_a/tas_global_SSP3_7_0.csv (red line and red shading)
• Data file: panel_a/tas_global_SSP5_8_5.csv (brown line)

Panel b: Sea-Ice Area

• Data file: panel_b/sia_arctic_september_Historical.csv (black line and grey shading)
• Data file: panel_b/sia_arctic_september_SSP1_1_9.csv (cyan line)
• Data file: panel_b/sia_arctic_september_SSP1_2_6.csv (blue line and blue shading)
• Data file: panel_b/sia_arctic_september_SSP2_4_5.csv (orange line)
• Data file: panel_b/sia_arctic_september_SSP3_7_0.csv (red line and red shading)
• Data file: panel_b/sia_arctic_september_SSP5_8_5.csv (brown line)

Panel c: Ocean Surface pH

• Data file: panel_c/phos_global_Historical.csv (black line and grey shading
• Data file: panel_c/phos_global_SSP1_1_9.csv (cyan line
• Data file: panel_b/phos_global_SSP1_2_6.csv (blue line and blue shading)
• Data file: panel_c/phos_global_SSP2_4_5.csv (orange line)
• Data file: panel_c/phos_global_SSP3_7_0.csv (red line and red shading)
• Data file: panel_c/phos_global_SSP5_8_5.csv (brown line)

Panel d: Sea Level

• Data file: panel_d/global_sea_level_observed.csv (black line)
• Data file: panel_d/global_sea_level_projected.csv (cyan, blue, orange, red and brown lines, red and blue shading)

Panel e: Sea Level

• Data file: panel_e: global_sea_level_2300_assessed.csv (columns 2 and 3, SSP1-2.6 scenario; columns 4 to 6 SSP5-8.5 scenario)

Sources of additional information

The following weblinks are provided in the Related Documents section of this catalogue record:

• Link to the report component containing the figure (Summary for Policymakers)

Sobek summary table.- total.contigs: total number of contigs in transcriptome- never.suspected: number of transcripts that were never suspected of being a cross-contamination- nb.suspects: number of transcripts that were suspected of being a cross-contamination- nb.clean: number of transcripts whose origin is from the focal sample- nb.lowcov: number of transcripts whose expression levels are too low in all samples- nb.overexp: number of transcripts whose expression levels are very high in at least 3 samples (often reflect highly conserved genes such as ribosomal gene, or external contamination shared by several samples)- nb.dubious: number of transcripts whose expression levels are too close between focal and alien samples to determine the true origin of the transcript- nb.contam: number of transcripts whose origin is from an alien sample of the same experiment

The subject matter in the five individual files which comprise the total data package is similar. SA1 presents detailed kind-of- business statistics (two-, three-, and four-digit industry levels) on number of establishments and receipts (total and with payroll), number of proprietorships and partnerships, annual and first quarter payroll, and number of paid employees. SA2 contains the same data items as above for selected services total, in addition to the number of establishments and receipt s for five major kind-of-business groups. SA3 contains number of establishments and receipts for selected services total and for 130 kind-of- business classifications. SA4 presents receipts and rank by volume of receipts. SA5 statistics are given by city size for number of incorporated cities, total population, number of establishments, receipts, yearly payroll, and the percent of total by population and sales.

Each of the files has slightly different geography for which summaries are presented. SA1 has summaries for the United States, divisions, States, SCA's and SMSA's, and counties and cities with over 300 service establishments. SA2 presents summary counts for each city of 2,500 inhabitants or more and for remainder of county. SA3 has summaries for the United States, regions, divisions, and States. SA4 presents summaries for the 250 largest counties and cities. SA5 presents United States tot al.

Data pertain to the date of the census, 1972. The first major enumeration of Selected Service establishments covered 1933. Censuses were also taken in 1939, 1948, and in 5 year intervals since

NBA data ranging from 1996 to 2024 contains physical attributes, bio information, (advanced) stats, and positions of players.

No missing values, certain data preprocessing will be needed depending on the task.

Data was gathered from the nba.com and Basketball Reference - starting with the season 1996/97 and up until the latest season 2023/24.

A lot of options for EDA & ML present - analyzing the change of physical attributes by position, how the number of 3-point shots changed throughout years, how the number of foreign players increased; using Machine Learning to predict player's points, rebounds and assists, predicting player's position, player clustering, etc.

The issue with the data was that the data about player height and weight was in Imperial system, so the scatterplot of heights and weights was not looking good (around only 20 distinct values for height and around 150 for weight, which is quite bad for the dataset of 13.000 players). I created a script in which I assign a random height to the player between 2 heights (let's say between 200.66 cm and 203.2 cm, which would be 6-7 and 6-8 in Imperial system), but I did it in a way that 80% of values fall in the range of 5 to 35% increase, which still keeps the integrity of the data (average height of the whole dataset increased for less than 1 cm). I did the same thing for the weight: since difference between 2 pounds is around 0.44 kg, I would assign a random value for weight for each player that is either +/- 0.22 from his original weight. Here I observed a change in the average weight of the whole dataset of around 0.09 kg, which is insignificant.

Unfortunately the NBA doesn't provide the data in cm and kg, and although this is not the perfect approach regarding accuracy, it is still much better than assigning only 20 heights to the dataset of 13.000 players.

This collection contains two types of records. Record 1 provides the number of workers identified by county of residence and county of employment. In the case of the six New England states (Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, and Vermont), cities and towns rather than counties are the unit of geography. Record 2 correlates the metropolitan area codes used in Record 1 with their alphabetic names and Metropolitan Statistical Area/Primary Metropolitan Statistical Area (MSA/PMSA) designations. (Source: ICPSR, retrieved 06/15/2011)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR06123.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

*denotes a significant deviation from Hardy-Weinberg equilibrium (P<0.05).

*****Documentation Process***** 1. Data Preparation: - Upload the data into Power Query to assess quality and identify duplicate values, if any. - Verify data quality and types for each column, addressing any miswriting or inconsistencies. 2. Data Management: - Duplicate the original data sheet for future reference and label the new sheet as the "Working File" to preserve the integrity of the original dataset. 3. Understanding Metrics: - Clarify the meaning of column headers, particularly distinguishing between Impressions and Reach, and comprehend how Engagement Rate is calculated. - Engagement Rate formula: Total likes, comments, and shares divided by Reach. 4. Data Integrity Assurance: - Recognize that Impressions should outnumber Reach, reflecting total views versus unique audience size. - Investigate discrepancies between Reach and Impressions to ensure data integrity, identifying and resolving root causes for accurate reporting and analysis. 5. Data Correction: - Collaborate with the relevant team to rectify data inaccuracies, specifically addressing the discrepancy between Impressions and Reach. - Engage with the concerned team to understand the root cause of discrepancies between Impressions and Reach. - Identify instances where Impressions surpass Reach, potentially attributable to data transformation errors. - Following the rectification process, meticulously adjust the dataset to reflect the corrected Impressions and Reach values accurately. - Ensure diligent implementation of the corrections to maintain the integrity and reliability of the data. - Conduct a thorough recalculation of the Engagement Rate post-correction, adhering to rigorous data integrity standards to uphold the credibility of the analysis. 6. Data Enhancement: - Categorize Audience Age into three groups: "Senior Adults" (45+ years), "Mature Adults" (31-45 years), and "Adolescent Adults" (<30 years) within a new column named "Age Group." - Split date and time into separate columns using the text-to-columns option for improved analysis. 7. Temporal Analysis: - Introduce a new column for "Weekend and Weekday," renamed as "Weekday Type," to discern patterns and trends in engagement. - Define time periods by categorizing into "Morning," "Afternoon," "Evening," and "Night" based on time intervals. 8. Sentiment Analysis: - Populate blank cells in the Sentiment column with "Mixed Sentiment," denoting content containing both positive and negative sentiments or ambiguity. 9. Geographical Analysis: - Group countries and obtain additional continent data from an online source (e.g., https://statisticstimes.com/geography/countries-by-continents.php). - Add a new column for "Audience Continent" and utilize XLOOKUP function to retrieve corresponding continent data.

*****Drawing Conclusions and Providing a Summary*****

• The data is equally distributed across different categories, platforms, and over the years.
• Most of our audience comprises senior adults (aged 45 and above).
• Most of our audience exhibit mixed sentiments about our posts. However, an equal portion expresses consistent sentiments.
• The majority of our posts were located in Africa.
• The number of posts increased from the first year to the second year and remained relatively consistent for the third year.
• The optimal time for posting is during the night on weekdays.
• The highest engagement rates were observed in Croatia then Malawi.
• The number of posts targeting senior adults is significantly higher than the other two categories. However, the engagement rates for mature and adolescent adults are also noteworthy, based on the number of targeted posts.

The global social media penetration rate in was forecast to continuously increase between 2024 and 2028 by in total 11.6 (+18.19 percent). After the ninth consecutive increasing year, the penetration rate is estimated to reach 75.31 and therefore a new peak in 2028. Notably, the social media penetration rate of was continuously increasing over the past years.

THE MISSION

The story behind the dataset is how to apply LSTM architecture to understand and apply multiple variables together to contribute more accuracy towards forecasting.

THE CONTENT

Air Pollution Forecasting The Air Quality dataset.

This is a dataset that reports on the weather and the level of pollution each hour for five years at the US embassy in Beijing, China.

The data includes the date-time, the pollution called PM2.5 concentration, and the weather information including dew point, temperature, pressure, wind direction, wind speed and the cumulative number of hours of snow and rain. The complete feature list in the raw data is as follows:

No: row number year: year of data in this row month: month of data in this row day: day of data in this row hour: hour of data in this row pm2.5: PM2.5 concentration DEWP: Dew Point TEMP: Temperature PRES: Pressure cbwd: Combined wind direction Iws: Cumulated wind speed Is: Cumulated hours of snow Ir: Cumulated hours of rain We can use this data and frame a forecasting problem where, given the weather conditions and pollution for prior hours, we forecast the pollution at the next hour.